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Sentiment analysis laser #274

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sentiment analysis using laser encoders
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39 changes: 39 additions & 0 deletions tasks/SentimentAnalysis/README.md
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# Laser Encoder: Sentiment Analysis

## Overview

This project demonstrates the application of the Laser Encoder tool for creating sentence embeddings in the context of sentiment analysis. The Laser Encoder is used to encode text data, and a sentiment analysis model is trained to predict the sentiment of the text.

## Getting Started

To run the notebook in Google Colab, simply click the "Open in Colab" button below:

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/NIXBLACK11/LASER-fork/blob/Sentiment-analysis-laser/tasks/SentimentAnalysis/SentimentAnalysis.ipynb)

## Example Usage

1. Alternative Download Instructions:
Manual Download and Extraction Steps:
- Download the sample dataset from the following link: [Sample Dataset](https://www.kaggle.com/datasets/abhi8923shriv/sentiment-analysis-dataset)

- Once the dataset is downloaded, locate the downloaded zip file on your local machine.
Unzip the file using a suitable tool (e.g., WinRAR, 7-Zip, or the built-in extraction tools on your operating system).
- Access the Extracted Files:
Navigate into the extracted folder to access the contents of the dataset.
- Use the Train.csv File.

2. Run the Example Notebook:
Execute the provided Jupyter notebook SentimentAnalysis.ipynb

jupyter notebook SentimentAnalysis.ipynb


## Customization

- Modify the model architecture, hyperparameters, and training settings in the neural network model section based on your requirements.
- Customize the sentiment mapping and handling of unknown sentiments in the data preparation section.

## Additional Notes
- Feel free to experiment with different models, embeddings, and hyperparameters to optimize performance.
- Ensure that the dimensions of embeddings and model inputs are compatible.
Adapt the code based on your specific dataset and use case.
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