https://github.com/patrikasvanagas/bi-lstm-glove-sentiment-classification

End-to-end review sentiment classification with text preprocessing, Bidirectional Long Short-Term Memory networks and Glove embeddings.
https://github.com/patrikasvanagas/bi-lstm-glove-sentiment-classification

bi-lstm bidirectional-lstm bidirectional-rnn glove glove-embeddings keras long-short-term-memory lstm lstm-sentiment-analysis natural-language-processing sentiment-analysis sentiment-classification text-classification text-processing

Last synced: 10 days ago
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End-to-end review sentiment classification with text preprocessing, Bidirectional Long Short-Term Memory networks and Glove embeddings.

• Host: GitHub
• URL: https://github.com/patrikasvanagas/bi-lstm-glove-sentiment-classification
• Owner: patrikasvanagas
• License: mit
• Created: 2023-06-05T17:38:55.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-12-03T17:27:43.000Z (about 1 year ago)
• Last Synced: 2024-11-28T19:18:16.789Z (2 months ago)
• Topics: bi-lstm, bidirectional-lstm, bidirectional-rnn, glove, glove-embeddings, keras, long-short-term-memory, lstm, lstm-sentiment-analysis, natural-language-processing, sentiment-analysis, sentiment-classification, text-classification, text-processing
• Language: Jupyter Notebook
• Homepage:
• Size: 435 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Bidirectional-LSTM-Sentiment-Classification

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1xPGlzrhixdJjNDbuj2FYJLvE1wG7nql7?pli=1)

This repository provides an example of an end-to-end approach to sentiment classification using [Bidirectional](https://en.wikipedia.org/wiki/Bidirectional_recurrent_neural_networks) [Long Short-Term Memory](https://en.wikipedia.org/wiki/Long_short-term_memory) networks with pre-trained [Glove embeddings](https://nlp.stanford.edu/projects/glove/). The workflow involves several preprocessing steps, including expansion of contractions and extraction of sentiment from multiple files, before implementing the Bi-LSTM model for classification. Additionally, experiments with early stopping, test sequences and cross-validation are carried out. The dataset, available in this repository, originates from the following paper:

> Ding, X., Liu, B. and Yu, P. S. (2008) ‘A Holistic Lexicon-Based Approach to Opinion Mining’. In *Proceedings of the 2008 International Conference on Web Search and Data Mining (WSDM '08)*. [DOI: 10.1145/1341531.1341561](https://dl.acm.org/doi/10.1145/1341531.1341561).

To run the notebook with access to a free GPU, click on the 'Open In Colab' badge above.