Lectures/L-NLP/01-Twitter-nlp.ipynb

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     "end_time": "2020-11-02T16:20:36.095077Z",
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       "<script>\n",
       "// Converts integer to roman numeral\n",
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       "        roman = '',\n",
       "        i;\n",
       "    for ( i in lookup ) {\n",
       "        while ( num >= lookup[i] ) {\n",
       "        roman += i;\n",
       "        num -= lookup[i];\n",
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       "    return roman;\n",
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       "\n",
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       "        toc += (new Array(level - openLevel + 1)).join(\"</ul>\");\n",
       "        for (i=level;i>openLevel;i--){levels[i]=0;}\n",
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       "\n",
       "        level = parseInt(openLevel);\n",
       "\n",
       "        if (this.id==''){this.id = this.innerHTML.replace(/ /g,\"-\")}\n",
       "        var anchor = this.id;\n",
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       "        toc += '<li><a href=\"#' + escape(anchor) + '\">'\n",
       "        + romanize(levels[openLevel].toString()) + '. ' + titleText\n",
       "        + '</a></li>';\n",
       "\n",
       "    });\n",
       "\n",
       "    if (level) {\n",
       "    toc += (new Array(level + 1)).join(\"</ul>\");\n",
       "    }\n",
       "\n",
       "    $('#'+toc_tag).append(toc);\n",
       "};\n",
       "\n",
       "$('<div id=\"toc\"></div>').css({position: 'fixed', top: '160px', right: 20, background: \"rgba(255, 255, 255, 0.6)\"}).appendTo(document.body);\n",
       "$(\"#toc\").css(\"z-index\", \"2000\");\n",
       "\n",
       "// Executes the createToc function\n",
       "setTimeout(function(){createTOC('toc', 1 + 3);},100);\n",
       "setTimeout(function(){createTOC('toc', 1 + 3);},5000);\n",
       "setTimeout(function(){createTOC('toc', 1 + 3);},15000);\n",
       "\n",
       "// Rebuild TOC every 5 minutes\n",
       "setInterval(function(){createTOC('toc', 1 + 3);}, 300000);\n",
       "\n",
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       "     $('#toc').hide('500');\n",
       "     $('#tocButton').val('Show table of content')\n",
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       "     $('#toc').show('500');\n",
       "     $('#tocButton').val('Hide table of content')\n",
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       "\n",
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       "    $('<form action=\"javascript:toc_toggle()\" id=\"tocButtonForm\"><input type=\"submit\" id=\"tocButton\" value=\"Hide table of content\"></form>').css({position: 'fixed', top: \"135px\", right: \"20px\", background: \"rgba(255, 255, 255, 0.6)\"}).appendTo(document.body);\n",
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       "    document.getElementById('tocButtonForm').style.top = \"135px\";\n",
       "    document.getElementById('tocButtonForm').style.right = \"20px\";\n",
       "    $('#tocButton').val('Hide table of content')\n",
       "}\n",
       "\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from nbindex import floating\n",
    "floating.tableofcontent()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## For previous topics\n",
    "\n",
    "Datavisualization manuals:\n",
    "* https://python-graph-gallery.com/\n",
    "* https://www.data-to-viz.com/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regular expressions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The fastest way to find patterns in strings is to use a library that can match so-called regular expressions. In Python, it is the `re` library that does the trick, but regular expressions are also common in other languages, and the syntax remains almost identical in every case. \n",
    "\n",
    "You can even use regexp search and replace in most text editors, for example while editing LaTeX files, or code on your computer. `grep` and `sed` also understand regular expressions in the terminal. Thus, once mastered, regexps are going to appear in almost each and every one of your works or data cleaning. Below, some common everyday examples are shown, alongside with the Python usage.\n",
    "\n",
    "As to learning the syntax, check out the first two links:\n",
    "\n",
    "* This is a quite good introduction:\n",
    "http://www.zytrax.com/tech/web/regex.htm\n",
    "\n",
    "* This is a really good tutorial that shows how regular expressions match or do not match:\n",
    "https://regexone.com/\n",
    "\n",
    "* And this link covers Pythons regex library as a starter.\n",
    "https://developers.google.com/edu/python/regular-expressions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A cheat sheet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Some Definitions\n",
    "\n",
    "* **literal** \tA literal is any character we use in a search or matching expression, for example, to find ind in windows the ind is a literal string - each character plays a part in the search, it is literally the string we want to find.\n",
    "* **metacharacter** \tA metacharacter is one or more special characters that have a unique meaning and are NOT used as literals in the search expression, for example, the character ^ (circumflex or caret) is a metacharacter.\n",
    "* **target string** \tThis term describes the string that we will be searching, that is, the string in which we want to find our match or search pattern.\n",
    "* **search expression** \tMost commonly called the regular expression. This term describes the search expression that we will be using to search our target string, that is, the pattern we use to find what we want.\n",
    "* **escape sequence** \tAn escape sequence is a way of indicating that we want to use one of our metacharacters as a literal. In a regular expression an escape sequence involves placing the metacharacter \\ (backslash) in front of the metacharacter that we want to use as a literal, for example, if we want to find (s) in the target string window(s) then we use the search expression \\(s\\) and if we want to find \\\\file in the target string c:\\\\file then we would need to use the search expression \\\\\\\\file (each \\ we want to search for as a literal (there are 2) is preceded by an escape sequence \\)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **\\[ \\]**  \n",
    "Match anything inside the square brackets for ONE character position, once and only once. For example, [12] means match the target to 1 and if that does not match then match the target to 2 while [0123456789] means match to any character in the range 0 to 9.\n",
    "* **-**  \n",
    "The - (dash) inside square brackets is the 'range separator' and allows us to define a range, in our example above of [0123456789] we could rewrite it as [0-9].\n",
    "\n",
    "* **^**  \n",
    "The ^ (circumflex or caret) inside square brackets negates the expression (we will see an alternate use for the circumflex/caret outside square brackets later), for example, [^Ff] means anything except upper or lower case F and [^a-z] means everything except lower case a to z."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **?**  \n",
    "The ? (question mark) matches when the preceding character occurs 0 or 1 times only, for example, colou?r will find both color (u is found 0 times) and colour (u is found 1 time).\n",
    "* **\\***  \n",
    "The * (asterisk or star) matches when the preceding character occurs 0 or more times, for example, tre* will find tree (e is found 2 times) and tread (e is found 1 time) and trough (e is found 0 times and thus returns a match only on the tr).\n",
    "* **+**  \n",
    "The + (plus) matches when the preceding character occurs 1 or more times, for example, tre+ will find tree (e is found 2 times) and tread (e is found 1 time) but NOT trough (0 times)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Backslash Sequences\n",
    "* \\d \tMatch any character in the range 0 - 9 (equivalent of POSIX [:digit:])\n",
    "* \\D \tMatch any character NOT in the range 0 - 9 (equivalent of POSIX [^[:digit:]])\n",
    "* \\s \tMatch any whitespace characters (space, tab etc.). (equivalent of POSIX [:space:] EXCEPT VT is not recognized)\n",
    "* \\S \tMatch any character NOT whitespace (space, tab). (equivalent of POSIX [^[:space:]]).\n",
    "* \\w \tMatch any character in the range 0 - 9, A - Z, a - z and underscore (_) [0-9A-Za-z_]. Equivalent of POSIX [:word:])\n",
    "* \\W \tMatch any character NOT the range 0 - 9, A - Z, a - z and punctuation (equivalent of POSIX [^[:graph:]])\n",
    "#### Positional Abbreviations\n",
    "* \\b \tWord boundary. Match any character(s) at the beginning (\\bxx) and/or end (xx\\b) of a word, thus \\bton\\b will find ton but not tons, but \\bton will find tons.\n",
    "* \\B \tNot word boundary. Match any character(s) NOT at the beginning(\\Bxx) and/or end (xx\\B) of a word, thus \\Bton\\B will find wantons but not tons, but ton\\B will find both wantons and tons."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The `re` library in Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:28.930491Z",
     "start_time": "2020-11-02T15:29:28.924916Z"
    }
   },
   "outputs": [],
   "source": [
    "# importing the library\n",
    "import re"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a hello world example for the `re` library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:28.946231Z",
     "start_time": "2020-11-02T15:29:28.934052Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['apple', 'pear', 'plum', 'cherries']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "re.findall('\\w+','apple pear plum cherries')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T22:18:29.178607Z",
     "start_time": "2020-11-02T22:18:29.173240Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['apple', 'pear', 'plum', 'cherries', 'are', 'tasty']"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "re.findall('\\w+','apple pear plum cherries are tasty!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The file `robinson.txt` contains Defoe's famous novel downloaded from the website of the [Project Gutenberg](http://www.gutenberg.org). Let's count how many times the pronoun `the` appears in the novel!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:28.964626Z",
     "start_time": "2020-11-02T15:29:28.949237Z"
    }
   },
   "outputs": [],
   "source": [
    "robinson = open('data/robinson.txt').read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:28.977145Z",
     "start_time": "2020-11-02T15:29:28.966966Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20623"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(re.findall('the',robinson))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's extract possible names beginning with a capital `F` from the novel.We exclude words that are supposed to be at the beginning of a sentence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:28.994734Z",
     "start_time": "2020-11-02T15:29:28.979442Z"
    }
   },
   "outputs": [],
   "source": [
    "# this is a find and replace command for strings\n",
    "# arguments are old_pattern, new_pattern, string\n",
    "# returns substituted string\n",
    "robinson_no_newlines = re.sub('\\n',' ',robinson)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because of the above excluding conditions, we check the preceding two characters of the names. But we would only like to return the names themselves without the preceding characters. Therefore, we use group capturing by putting brackets around the desired parts `()`, which makes sure that `re.findall` only returns the matched groups, not the whole pattern. If we capture more than one group, the returned list would contain tuples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:29.042579Z",
     "start_time": "2020-11-02T15:29:28.997173Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Father',\n",
       " 'February',\n",
       " 'Fernand',\n",
       " 'Finland',\n",
       " 'Firma',\n",
       " 'First',\n",
       " 'Flanders',\n",
       " 'Fontarabia',\n",
       " 'Formosa',\n",
       " 'Fowling',\n",
       " 'France',\n",
       " 'French',\n",
       " 'Frenchman',\n",
       " 'Friday',\n",
       " 'Frozen',\n",
       " 'Frye',\n",
       " 'Fyal'}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(re.findall('[^\\.!\\?\" ] (F[a-z]+)', robinson_no_newlines))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:29.090360Z",
     "start_time": "2020-11-02T15:29:29.046916Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'  Friday, ',\n",
       " ' \"Friday, ',\n",
       " ' \"Friday; ',\n",
       " ' \"Friday? ',\n",
       " ', Friday!\"',\n",
       " ', Friday, ',\n",
       " ', Friday,\"',\n",
       " ', Friday?\"',\n",
       " '; Friday, '}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(re.findall('[^\\w][^\\w]Friday[^\\w][^\\w]',robinson_no_newlines))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some command line examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T22:23:35.509562Z",
     "start_time": "2020-11-02T22:23:35.342112Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    1 10000 50000 data/random.txt\r\n"
     ]
    }
   ],
   "source": [
    "!wc data/random.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reformatting the output of `uniq -c`!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:29.235514Z",
     "start_time": "2020-11-02T15:29:29.093848Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     45 0.00\r\n",
      "    101 0.01\r\n",
      "    103 0.02\r\n",
      "    104 0.03\r\n",
      "    109 0.04\r\n",
      "     85 0.05\r\n",
      "    101 0.06\r\n",
      "     97 0.07\r\n",
      "     99 0.08\r\n",
      "    107 0.09\r\n"
     ]
    }
   ],
   "source": [
    "!cat data/random.txt | sed 's/ /\\n/g' | sort -n | uniq -c |head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:29.365389Z",
     "start_time": "2020-11-02T15:29:29.239139Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "45,0.00\r\n",
      "101,0.01\r\n",
      "103,0.02\r\n",
      "104,0.03\r\n",
      "109,0.04\r\n",
      "85,0.05\r\n",
      "101,0.06\r\n",
      "97,0.07\r\n",
      "99,0.08\r\n",
      "107,0.09\r\n"
     ]
    }
   ],
   "source": [
    "!cat data/random.txt | sed 's/ /\\n/g' | sort -n | uniq -c | sed -e 's/^\\s*//g' | sed -e 's/ /,/g' | head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adding `0.` to floats smaller than `1`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:29.486805Z",
     "start_time": "2020-11-02T15:29:29.368787Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# This is a sample file for illustration purposes.\r\n",
      "# This is a comment line.\r\n",
      "But this is not, it just happens to contain a '#'.\r\n",
      "0.3\r\n",
      "0.4\r\n",
      "0.64\r\n",
      "1.45\r\n",
      "2.5\r\n"
     ]
    }
   ],
   "source": [
    "!cat data/sample.txt | sed -e 's/^\\./0\\./g'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Excluding comment lines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:29.606998Z",
     "start_time": "2020-11-02T15:29:29.490179Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "But this is not, it just happens to contain a '#'.\r\n",
      ".3\r\n",
      ".4\r\n",
      ".64\r\n",
      "1.45\r\n",
      "2.5\r\n"
     ]
    }
   ],
   "source": [
    "!cat data/sample.txt | grep -ve '^#'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How to feed a text to the computer?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First of all, we want to decide what elements we want as building blocks. A common approach is to break the text down to words. But even this task is not that simple as it first seems. The next string contains an example message of Donald Trump from the Twitter social network.\n",
    "\n",
    "Possible problems:\n",
    "* lower and uppercase\n",
    "* punctuation\n",
    "* emojis\n",
    "* character encoding\n",
    "* named entity recognition..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:29.615082Z",
     "start_time": "2020-11-02T15:29:29.610533Z"
    }
   },
   "outputs": [],
   "source": [
    "trump_tweet = 'Having great meetings and discussions with my friend, \\\n",
    "    President @EmmanuelMacron of France. We are in the midst of meetings \\\n",
    "    on Iran, Syria and Trade. We will be holding a joint press conference shortly, \\\n",
    "    here at the @WhiteHouse. 🇺🇸🇫🇷'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Does this work?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:29.626028Z",
     "start_time": "2020-11-02T15:29:29.617865Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "having great meetings and discussions with my friend president emmanuelmacron of france we are in the midst of meetings on iran syria and trade we will be holding a joint press conference shortly here at the whitehouse\n"
     ]
    }
   ],
   "source": [
    "print(' '.join(re.findall('\\w+',trump_tweet.lower())))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Natural Language Processing\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## nltk\n",
    "Natural Language Toolkit - https://www.nltk.org/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"NLTK is a leading platform for building Python programs to work with human language data. It provides easy-to-use interfaces to over 50 corpora* and lexical resources such as [WordNet](https://wordnet.princeton.edu/), along with a suite of text processing libraries for classification[[1](https://monkeylearn.com/what-is-text-classification/)][[2](https://towardsdatascience.com/machine-learning-nlp-text-classification-using-scikit-learn-python-and-nltk-c52b92a7c73a)], tokenization[[1](https://www.geeksforgeeks.org/nlp-how-tokenizing-text-sentence-words-works/)][[2](https://text-processing.com/demo/tokenize/)], stemming[[1](https://www.geeksforgeeks.org/introduction-to-stemming/)][[2](https://www.datacamp.com/community/tutorials/stemming-lemmatization-python)], tagging, parsing, and semantic reasoning, wrappers for industrial-strength NLP libraries, and an active discussion forum.\"\n",
    "\n",
    "\\*all the writings or works of a particular kind or on a particular subject, especially the complete works of an author"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:30.708736Z",
     "start_time": "2020-11-02T15:29:29.628581Z"
    }
   },
   "outputs": [],
   "source": [
    "import nltk"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at our former task and find the pieces that carry any information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:30.730605Z",
     "start_time": "2020-11-02T15:29:30.712305Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "having great meetings and discussions with my friend , president @ emmanuelmacron of france . we are in the midst of meetings on iran , syria and trade . we will be holding a joint press conference shortly , here at the @ whitehouse . 🇺🇸🇫🇷\n"
     ]
    }
   ],
   "source": [
    "print(' '.join(nltk.word_tokenize(trump_tweet.lower())))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look for a good tweet tokenizer that has Twitter-specific regular expressions built-in either within `nltk` or outside!\n",
    "http://www.nltk.org/api/nltk.tokenize.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tokenizers\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:30.740451Z",
     "start_time": "2020-11-02T15:29:30.733179Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Let', \"'\", 's', 'see', 'how', 'it', \"'\", 's', 'working', '.']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from nltk.tokenize import WordPunctTokenizer\n",
    "\n",
    "#  WordPunctTokenizer – It seperates the punctuation from the words.\n",
    "tokenizer = WordPunctTokenizer() \n",
    "tokenizer.tokenize(\"Let's see how it's working.\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:30.752273Z",
     "start_time": "2020-11-02T15:29:30.743066Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Hello | everyone. | Welcome | to | this | lecture | ! | We | have | a | pleasant | sunny | weather | today | , | do | n't | we | ?\""
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from nltk.tokenize import TreebankWordTokenizer \n",
    "\n",
    "# These tokenizers work by separating the words using punctuation and spaces,\n",
    "# and it does not discard the punctuation, allowing a user to decide \n",
    "# what to do with the punctuations at the time of pre-processing.\n",
    "\n",
    "tokenizer = TreebankWordTokenizer() \n",
    "text = \"Hello everyone. Welcome to this lecture! We have a pleasant sunny weather today, don't we?\"\n",
    "\" | \".join(tokenizer.tokenize(text))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:30.766171Z",
     "start_time": "2020-11-02T15:29:30.755900Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\"Let's\", 'see', 'how', \"it's\", 'working']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from nltk.tokenize import RegexpTokenizer \n",
    "\n",
    "# tokenizes according to the given regular expression\n",
    "\n",
    "tokenizer = RegexpTokenizer(\"[\\w']+\") \n",
    "text = \"Let's see how it's working.\"\n",
    "tokenizer.tokenize(text) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:30.780618Z",
     "start_time": "2020-11-02T15:29:30.769075Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['L', 't', 's s', ' how it', 's working.']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer = RegexpTokenizer(\"[^e']+\") \n",
    "tokenizer.tokenize(text) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:30.796258Z",
     "start_time": "2020-11-02T15:29:30.783645Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['@remy',\n",
       " ':',\n",
       " 'This',\n",
       " 'is',\n",
       " 'waaayyy',\n",
       " 'too',\n",
       " 'much',\n",
       " 'for',\n",
       " 'you',\n",
       " '!',\n",
       " '!',\n",
       " '!']"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from nltk.tokenize import  TweetTokenizer\n",
    "\n",
    "tokenizer = TweetTokenizer(strip_handles=False, reduce_len=True)\n",
    "s1 = '@remy: This is waaaaayyyy too much for you!!!!!!'\n",
    "tokenizer.tokenize(s1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:30.809593Z",
     "start_time": "2020-11-02T15:29:30.799114Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['@remy',\n",
       " ':',\n",
       " 'This',\n",
       " 'is',\n",
       " 'waaaaayyyy',\n",
       " 'too',\n",
       " 'much',\n",
       " 'for',\n",
       " 'you',\n",
       " '!',\n",
       " '!',\n",
       " '!']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer = TweetTokenizer(strip_handles=False)\n",
    "s1 = '@remy: This is waaaaayyyy too much for you!!!!!!'\n",
    "tokenizer.tokenize(s1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Text normalization\n",
    "Stemming and Lemmatization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we are interested in the core meaning, often we can throw away third person forms or plurals etc.\n",
    "* **Stemming** allows the computer to group together words according to their various inflections that are tagged with a particular stem. For instance: “walk,” “walked” and “walking.”\n",
    "* **Lemmatization** is a bit more complex in that the computer can group together words that do not have the same stem, but still have the same inflected meaning. Grouping the word “good” with words like “better” and “best” is an example of lemmatization\n",
    "\n",
    "Further Reading:\n",
    "* https://www.datacamp.com/community/tutorials/stemming-lemmatization-python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:30.833354Z",
     "start_time": "2020-11-02T15:29:30.816502Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "having -> having\n",
      "great -> great\n",
      "meetings -> meet\n",
      "and -> and\n",
      "discussions -> discuss\n",
      "with -> with\n",
      "my -> my\n",
      "friend -> friend\n",
      ", -> ,\n",
      "president -> presid\n"
     ]
    }
   ],
   "source": [
    "from nltk.stem import SnowballStemmer\n",
    "\n",
    "snow = SnowballStemmer('english',ignore_stopwords=True)\n",
    "for word in nltk.word_tokenize(trump_tweet.lower())[0:10]:\n",
    "    print(word,'->',snow.stem(word))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Examples of Stemmer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:30.840694Z",
     "start_time": "2020-11-02T15:29:30.836612Z"
    }
   },
   "outputs": [],
   "source": [
    "from nltk.stem import PorterStemmer as porter,\\\n",
    "                      LancasterStemmer as lancaster,\\\n",
    "                      SnowballStemmer as snow\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:30.859244Z",
     "start_time": "2020-11-02T15:29:30.843611Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Word                Porter Stemmer      lancaster Stemmer   Snowball Stemmer    \n",
      "friend              friend              friend              friend              \n",
      "friendship          friendship          friend              friendship          \n",
      "friends             friend              friend              friend              \n",
      "friendships         friendship          friend              friendship          \n",
      "stabil              stabil              stabl               stabil              \n",
      "destabilize         destabil            dest                destabil            \n",
      "misunderstanding    misunderstand       misunderstand       misunderstand       \n",
      "railroad            railroad            railroad            railroad            \n",
      "moonlight           moonlight           moonlight           moonlight           \n",
      "football            footbal             footbal             footbal             \n",
      "troubling           troubl              troubl              troubl              \n",
      "connecting          connect             connect             connect             \n"
     ]
    }
   ],
   "source": [
    "#A list of words to be stemmed\n",
    "word_list = [\"friend\", \"friendship\", \"friends\", \"friendships\",\"stabil\",\"destabilize\",\"misunderstanding\",\n",
    "             \"railroad\",\"moonlight\",\"football\", \"troubling\", \"connecting\"]\n",
    "print(\"{0:20}{1:20}{2:20}{3:20}\".format(\"Word\",\"Porter Stemmer\",\"lancaster Stemmer\",\"Snowball Stemmer\"))\n",
    "for word in word_list:\n",
    "    print(\"{0:20}{1:20}{2:20}{3:20}\".format(word, porter().stem(word), lancaster().stem(word), snow('english').stem(word)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stop words\n",
    "Stop Words are words which do not contain important significance to be used in Search Queries. Usually, these words are filtered out from search queries because they return a vast amount of unnecessary information. Each language will give its own list of stop words to use. Mostly they are words that are commonly used in the English language such as 'as, the, be, are' etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### When to remove stop words?\n",
    "\n",
    "If we have a task of text classification or sentiment analysis then we should remove stop words as they do not provide any information to our model, i.e keeping out unwanted words out of our corpus, but if we have the task of language translation then stopwords are useful, as they have to be translated along with other words.\n",
    "\n",
    "There is no hard and fast rule on when to remove stop words. But I would suggest removing stop words if our task to be performed is one of Language Classification, Spam Filtering, Caption Generation, Auto-Tag Generation, Sentiment analysis, or something that is related to text classification.\n",
    "\n",
    "On the other hand, if our task is one of Machine Translation, Question-Answering problems, Text Summarization, Language Modeling, it’s better not to remove the stop words as they are a crucial part of these applications"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Further reading: \n",
    "* https://www.analyticsvidhya.com/blog/2019/08/how-to-remove-stopwords-text-normalization-nltk-spacy-gensim-python/\n",
    "* https://medium.com/@saitejaponugoti/stop-words-in-nlp-5b248dadad47"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:31.006878Z",
     "start_time": "2020-11-02T15:29:30.861686Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to /home/wfct0p/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package punkt to /home/wfct0p/nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "# importing NLTK libarary stopwords \n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "nltk.download('stopwords')\n",
    "nltk.download('punkt')\n",
    "from nltk.tokenize import word_tokenize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:31.014087Z",
     "start_time": "2020-11-02T15:29:31.009513Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', \"you're\", \"you've\", \"you'll\", \"you'd\", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', 'she', \"she's\", 'her', 'hers', 'herself', 'it', \"it's\", 'its', 'itself', 'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', \"that'll\", 'these', 'those', 'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', 'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', 'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after', 'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further', 'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more', 'most', 'other', 'some', 'such', 'no', 'nor', 'not', 'only', 'own', 'same', 'so', 'than', 'too', 'very', 's', 't', 'can', 'will', 'just', 'don', \"don't\", 'should', \"should've\", 'now', 'd', 'll', 'm', 'o', 're', 've', 'y', 'ain', 'aren', \"aren't\", 'couldn', \"couldn't\", 'didn', \"didn't\", 'doesn', \"doesn't\", 'hadn', \"hadn't\", 'hasn', \"hasn't\", 'haven', \"haven't\", 'isn', \"isn't\", 'ma', 'mightn', \"mightn't\", 'mustn', \"mustn't\", 'needn', \"needn't\", 'shan', \"shan't\", 'shouldn', \"shouldn't\", 'wasn', \"wasn't\", 'weren', \"weren't\", 'won', \"won't\", 'wouldn', \"wouldn't\"]\n"
     ]
    }
   ],
   "source": [
    "print(stopwords.words('english'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:31.036593Z",
     "start_time": "2020-11-02T15:29:31.016733Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Oh', 'man', ',', 'this', 'is', 'pretty', 'cool', '.', 'We', 'will', 'do', 'more', 'such', 'things', '.']\n",
      "['Oh', 'man', ',', 'pretty', 'cool', '.', 'We', 'things', '.']\n"
     ]
    }
   ],
   "source": [
    "# random sentecnce with lot of stop words\n",
    "sample_text = \"Oh man, this is pretty cool. We will do more such things.\"\n",
    "text_tokens = word_tokenize(sample_text)\n",
    "\n",
    "tokens_without_sw = [word for word in text_tokens if not word in stopwords.words('english')]\n",
    "\n",
    "print(text_tokens)\n",
    "print(tokens_without_sw)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore a document\n",
    "\n",
    "1.    Take a document as the input.\n",
    "2.    Read the document line by line\n",
    "3.    Tokenize the line -> list of words\n",
    "4.    Stem the words\n",
    "5.    Output the stemmed words (print on screen or write to a file)\n",
    "6.    Repeat step 2 to step 5 until it is to the end of the document.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Term-document matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A useful algebraic concept for representing a collection of texts is having a matrix. On this matrix, it is possible to try many concepts and methods from linear algebra, machine learning etc.\n",
    "\n",
    "https://en.wikipedia.org/wiki/Document-term_matrix\n",
    "\n",
    "<img src=\"https://qph.fs.quoracdn.net/main-qimg-27639a9e2f88baab88a2c575a1de2005\">\n",
    "\n",
    "Let us have a look at such a matrix on some Trump tweets again http://www.trumptwitterarchive.com/archive:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:32.919680Z",
     "start_time": "2020-11-02T15:29:31.039047Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import cufflinks as cf\n",
    "from IPython.display import display,HTML\n",
    "from ipywidgets import interact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:32.985351Z",
     "start_time": "2020-11-02T15:29:32.922983Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#We set the all charts as public\n",
    "cf.set_config_file(sharing='public',theme='pearl',offline=False)\n",
    "cf.go_offline()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:33.010311Z",
     "start_time": "2020-11-02T15:29:32.988021Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   hello  omg  pony  she  there  went  why\n",
      "0      2    0     0    0      1     0    1\n",
      "1      1    1     1    0      0     0    0\n",
      "2      0    1     0    1      1     1    0\n"
     ]
    }
   ],
   "source": [
    "vec = CountVectorizer()\n",
    "docs = ['why hello hello there', 'omg hello pony', 'she went there? omg']\n",
    "X = vec.fit_transform(docs)\n",
    "simple_df = pd.DataFrame(X.toarray(), columns=vec.get_feature_names())\n",
    "print(simple_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The file `corpus.txt` contains a small collection of wikipedia articles fromt he disciplines of chemical physics (21 articles), experimental physics (24 articles), and computational biology (18 articles).??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:33.033358Z",
     "start_time": "2020-11-02T15:29:33.013186Z"
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   },
   "outputs": [],
   "source": [
    "articles = pd.read_csv('data/corpus.txt',header=None)\n",
    "# articles = pd.read_csv('data/got_tweets.csv')\n",
    "# articles = pd.read_csv('data/obamacare.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:33.041171Z",
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       "0   the nuclear overhauser effect noe is the tran...\n",
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    "articles.head()"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make an array counting the occurrence of words in each article!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:29:33.072829Z",
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    "c = CountVectorizer()\n",
    "c.fit_transform(articles[0].head(3)).todense()"
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   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
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       "'above absorption abundance accomplished according achieved act actual addition aggregate albert all allowing already also although american an analytical and anderson angular another any appear applicable application are around as atomic atoms available awarded ball based basis be because been being below between biological bonds both branch branches but by can carver case centre certain change characterizing chemical chemistry close common commonly compensate complete component components compounds conditions conduction confirm conformation conjunction connected consequently consists constant controlled corresponds could coupled coupling cross currently decreases demonstrated demonstrating derived described determine did different differs dimensional dipole discovery distances do due each early effect effectively electron electronic electrons energy engineering enhanced entire equations essentially etc examined excitation exhibit experiment experimental experimentally explains explanation extension far field fine first following for formula freedom frequency from gap gas general generally give given gravity ground had has have helium help here higher highly however impossible impractical in increases infrared intensities inter into investigation involves irradiation is isotopes it its kinetic kinetics known light like linear liquid macromolecules magnetic make many mass matter mechanical medium metals methods microwave model molecular molecule molecules momenta most much nanotechnology natural nature necessary need nmr no nobel noe non not nuclear number object observation observe observed observing obvious occur occurs of often one only or order organic other otherwise overhauser particles peak peaks perhaps phenomenon photons physicist planck polarization population positions possible practice precise predicted prediction presence prize proceeds properties proposed published quantum radiation raman rather ratio referred region relaxation requires resonance results rigid rotational rotor rule see selection seminal shell should show shown similarly simultaneously slichter slightly small so solomon solution solvation solvent solvents some space spacing species spectra spectroscopic spectroscopy spectrum speed spin spins state states strongest structure structures studied subsequent successfully such superfluid suspended techniques than that the their then theoretical theoretically there these this three through thus to together tools totally transfer transferred transition transitions two under unpaired upon use used useful using variation vector verified very via viable vibration vibrational was well when where whereas which who will with without work would'"
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     "execution_count": 37,
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   "source": [
    "' '.join(c.get_feature_names())"
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   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
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     "end_time": "2020-11-02T15:29:33.136691Z",
     "start_time": "2020-11-02T15:29:33.086711Z"
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   },
   "outputs": [],
   "source": [
    "vec = CountVectorizer()\n",
    "X = vec.fit_transform(articles[0].head(100))\n",
    "df = pd.DataFrame(X.toarray(), columns=vec.get_feature_names())"
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  },
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   "cell_type": "code",
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    "df[['physics', 'molecule','chemistry']].iplot(kind='heatmap',colorscale='spectral',title='Term document matrix as a heatmap')"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's go back to the Trump tweets!\n",
    "\n",
    "First, we have to create our own tokenizer, because the built-in one leaves us with punctuation marks and numbers."
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "trump_tweets = pd.read_csv('data/trumptweets.csv')\n",
    "trump_tweets.dropna(subset=[\"text\"],inplace=True)"
   ]
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   "source": [
    "tt = nltk.tokenize.TweetTokenizer(preserve_case=True,strip_handles=True,reduce_len=True)\n",
    "ss = nltk.stem.SnowballStemmer('english')"
   ]
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    "def custom_tokenizer(text):\n",
    "    \"\"\"\n",
    "    The input is the string to tokenize.\n",
    "    \n",
    "    The output is the list of desired words.\n",
    "    \"\"\"\n",
    "    words = tt.tokenize(text)\n",
    "    return [ss.stem(w) for w in words if re.match('^[a-z]+$',w)!=None]"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The TF-IDF matrix\n",
    "term frequency - inverse document frequency is a numerical statistic that is intended to reflect how important a word is to a document in a collection or corpus.\n",
    "\n",
    "It is often used as a *weighting factor* in searches of information retrieval, *text mining*, and *user modeling*."
   ]
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     "start_time": "2020-11-02T15:29:33.656102Z"
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   },
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   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.decomposition import LatentDirichletAllocation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
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     "end_time": "2020-11-02T15:29:33.745335Z",
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   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-11-02 15:29:33,695 [15422] WARNING  py.warnings:99: [JupyterRequire] /opt/conda/lib/python3.7/site-packages/sklearn/feature_extraction/text.py:300: UserWarning:\n",
      "\n",
      "Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['abov', 'afterward', 'alon', 'alreadi', 'alway', 'ani', 'anoth', 'anyon', 'anyth', 'anywher', 'becam', 'becaus', 'becom', 'befor', 'besid', 'cri', 'describ', 'dure', 'els', 'elsewher', 'empti', 'everi', 'everyon', 'everyth', 'everywher', 'fifti', 'forti', 'henc', 'hereaft', 'herebi', 'howev', 'hundr', 'inde', 'mani', 'meanwhil', 'moreov', 'nobodi', 'noon', 'noth', 'nowher', 'onc', 'onli', 'otherwis', 'ourselv', 'perhap', 'pleas', 'sever', 'sinc', 'sincer', 'sixti', 'someon', 'someth', 'sometim', 'somewher', 'themselv', 'thenc', 'thereaft', 'therebi', 'therefor', 'togeth', 'twelv', 'twenti', 'veri', 'whatev', 'whenc', 'whenev', 'wherea', 'whereaft', 'wherebi', 'wherev', 'whi', 'yourselv'] not in stop_words.\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 28)\n"
     ]
    }
   ],
   "source": [
    "c = TfidfVectorizer(tokenizer=custom_tokenizer,stop_words='english',min_df=5,norm='l2')\n",
    "# articles.text.str.contains('Clinton')\n",
    "#X = c.fit_transform(articles[0])\n",
    "X = c.fit_transform(trump_tweets[trump_tweets.text.str.contains('Clinton')].text.iloc[:100])\n",
    "print(X.shape)\n",
    "words = c.get_feature_names()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fitting an LDA model to it.\n",
    "\n",
    "s a generative statistical model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar. For example, if observations are words collected into documents, it posits that each document is a mixture of a small number of topics and that each word's presence is attributable to one of the document's topics. LDA is an example of a topic model and belongs to the machine learning toolbox and in wider sense to the artificial intelligence toolbox. \n",
    "\n",
    "Further reading for the Latent Dirichlet Allocation model:\n",
    "* https://programmerbackpack.com/latent-dirichlet-allocation-for-topic-modelling-explained-algorithm-and-python-scikit-learn-implementation/\n",
    "* https://towardsdatascience.com/topic-modeling-and-latent-dirichlet-allocation-in-python-9bf156893c24\n",
    "* https://www.youtube.com/watch?v=Y79sCtzddyA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Topic Modelling** is an unsupervised Machine Learning task where we try to discover \"abstract topics\" that can describe a collection of documents. This means we have a collection of texts and we try to find patterns of words and phrases that can help us cluster the documents and group them by \"topics\".\n",
    "\n",
    "**Latent Dirichlet Allocation** is a type of Unsupervised Machine Learning. We don't know the topics of documents before we begin, we can only specify how many topics we want to find. At the end of the parsing we can look into the results and figure out if they are helpful or not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:51:10.606681Z",
     "start_time": "2020-11-02T15:51:10.299125Z"
    }
   },
   "outputs": [],
   "source": [
    "l = LatentDirichletAllocation(n_components=3,n_jobs=-1)\n",
    "T = l.fit_transform(X)\n",
    "W = l.components_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:51:12.967639Z",
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   "outputs": [],
   "source": [
    "df = pd.DataFrame()\n",
    "df['Weight in topic 1'] = W[0]\n",
    "df['Weight in topic 2'] = W[1]\n",
    "df['Weight in topic 3'] = W[2]\n",
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       "                            \n",
       "var gd = document.getElementById('eebab818-2f30-4614-9afa-0d2662ccfa95');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
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       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
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       "    x.observe(notebookContainer, {childList: true});\n",
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       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
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       "\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.set_index('Words').iplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T14:55:08.707441Z",
     "start_time": "2020-11-02T14:55:08.659437Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic 0:  thing video vs make bush new america hillari poll donald debat need trump clinton beat onli vote know realli like\n",
      "Topic 1:  hillaryclinton make like realli veri bush presid run thing america new need debat android vote twitter video great fals onli\n",
      "Topic 2:  veri like poll know run onli hillaryclinton twitter trump android donald presid realli fals hillari vote great clinton vs beat\n",
      " 2: "
     ]
    }
   ],
   "source": [
    "topics = pd.DataFrame()\n",
    "for i,line in enumerate(W):\n",
    "    topics[i] = [ words[ind]  for ind in np.argsort(line)[0:20] ]\n",
    "for T in topics.columns:\n",
    "    print(\"Topic %d: \"%T,' '.join(topics[T]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T14:54:14.358401Z",
     "start_time": "2020-11-02T14:54:14.354700Z"
    }
   },
   "outputs": [],
   "source": [
    "#!pip install --user wordcloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:26:25.130562Z",
     "start_time": "2020-11-02T15:26:24.815659Z"
    }
   },
   "outputs": [],
   "source": [
    "import wordcloud\n",
    "from wordcloud import WordCloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2020-11-02T15:26:13.181Z"
    }
   },
   "outputs": [],
   "source": [
    "wordcloud = WordCloud(width = 800, height = 800, \n",
    "                      background_color ='white',\n",
    "                      #stopwords = stopwords, \n",
    "                      min_font_size = 10).generate(' '.join(topics[1].values)) \n",
    "\n",
    "plt.clf();\n",
    "plt.figure(figsize = (10, 7), dpi=46, facecolor = None) \n",
    "plt.imshow(wordcloud) \n",
    "plt.axis(\"off\") \n",
    "plt.tight_layout(pad = 0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:04:33.654505Z",
     "start_time": "2020-11-02T15:04:33.092741Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 460x322 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf();\n",
    "plt.figure(figsize = (10, 7), dpi=46, facecolor = None) \n",
    "plt.imshow(wordcloud) \n",
    "plt.axis(\"off\") \n",
    "plt.tight_layout(pad = 0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T15:04:33.654505Z",
     "start_time": "2020-11-02T15:04:33.092741Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 460x322 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf();\n",
    "plt.figure(figsize = (10, 7), dpi=46, facecolor = None) \n",
    "plt.imshow(wordcloud) \n",
    "plt.axis(\"off\") \n",
    "plt.tight_layout(pad = 0) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A Venn- diagram"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Top 10 applications of NLP\n",
    "https://www.analyticsvidhya.com/blog/2020/07/top-10-applications-of-natural-language-processing-nlp/\n",
    "\n",
    "* autocomplete or autocorrect (search engines)\n",
    "* machine translation (google translate)\n",
    "* social media monitoring\n",
    "* chatbots for basic customer queries\n",
    "* survey analysis\n",
    "* targeted advertising\n",
    "* hiring and recruitment\n",
    "* voice assistants\n",
    "* grammar checker\n",
    "* email filtering (e.g. spam filtering)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* [Crowdbreaks](https://www.crowdbreaks.org/): Crowdbreaks is a health trend tracking system. Crowdbreaks collects tweets with keywords that might be related to specific health topics. By using natural language processing and machine learning techniques the system tries to filter relevant from non-relevant content. By providing more meta information (labelling) of the tweets, these algorithms will continuously improve to detect tweets which are truly relevant and understand their content.issues. \n",
    "\n",
    "* Sentiment analysis using social media: How people feel about topics, events or products [1](https://sproutsocial.com/insights/social-media-sentiment-analysis/) [2](https://blog.hootsuite.com/social-media-sentiment-analysis-tools/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other questions\n",
    "\n",
    "What to do when the texts do not have the same length? How to exclude not relevant words? How to make a weighting scheme for our purposes?\n",
    "\n",
    "How to deal with the 'timeseries' nature of texts? What inputs do neural networks expect?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}