This repository is a curated list of resources for sentiment analysis, targeting researchers and developers. It provides links to survey papers, baseline systems, datasets, and open-source implementations across various programming languages, aiming to facilitate both academic study and practical application integration.

How It Works

The list categorizes sentiment analysis approaches, including lexical, supervised machine learning (Naive Bayes, SVM), unsupervised machine learning (LDA, Word2Vec), and deep learning (CNN, LSTM, attention-based models). It highlights the evolution towards aspect-based sentiment analysis, which offers finer-grained insights than document or sentence-level analysis.

Quick Start & Requirements

This is a curated list, not a runnable application. No installation or specific requirements are listed.

Highlighted Details

• Comprehensive coverage of sentiment analysis techniques from lexical methods to advanced deep learning architectures.
• Links to key academic papers and datasets like SentiWordNet, GloVe, and Stanford Sentiment Treebank.
• Categorization of open-source implementations by programming language (Python, Java, NodeJS, etc.) and methodology.
• Includes a section on SaaS APIs and web applications for sentiment analysis.

Maintenance & Community

This is a community-curated list. Contributions are welcomed via pull requests. No specific maintainers or community channels are listed.

Licensing & Compatibility

The repository itself is not licensed as it is a list of links. The licenses of the linked resources would need to be checked individually.

Limitations & Caveats

The README warns that off-the-shelf implementations may not perform well across different domains and require validation on specific use cases.