/twitter_sentiment_analysis

Using TensorFlow: utilize LSTM networks and GloVe Word Embedding Vectors for analyzing sentiment on Twitter data.

Primary LanguagePureBasic

Twitter Sentiment Analysis

This project utilizes LSTM (Long Short-Term Memory) networks and GloVe (Global Vectors for Word Representation) Word Embedding Vectors for analyzing sentiment on Twitter data.

Installation

Before running the sentiment analysis, ensure you have the necessary dataset and dependencies installed.

Dataset

The GloVe word embedding vectors can be downloaded and unzipped using the following command:

curl -O http://downloads.cs.stanford.edu/nlp/data/glove.6B.zip
unzip glove.6B.zip -d path/to/destination

Dependencies

This project is compatible with CUDA 11.2 for GPU acceleration on Native Windows environments and requires TensorFlow version 2.10.0. Ensure you have the correct TensorFlow versions installed by running:

pip install tensorflow==2.10.0 tensorflow-gpu==2.10.0