Sentiment analysis laser

tasks/SentimentAnalysis/README.md

@@ -0,0 +1,39 @@
+# 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.