Collection of materials on Sentiment Analysis


  • SENTIMENT ANALYSIS OF TWITTER FEEDS by Yogesh Garg, Dr. Niladri Chatterjee: GitHub
  • GitHub|Sentiment Analysis on Twitte
  • Sentiment140 by okugami79: R package for Twitter sentiment text analysis: GitHub
  • Quora|How do I use the sentiment140 data set for doing sentiment analysis in Python
  • Sentiment Symposium Tutorial: Language and cognition by Christopher Potts, Stanford Linguistics, 2011: Homepage
  • Emotion Detection and Recognition from Text Using Deep Learning
  • List of Machine Learning and Deep Learning from GitHub: ZhiHu
  • Twitter-Sentiment-Analysis: GitHub, Videos
  • FFNN: feed-foward neural network: GitHub
    • This is an obsolete package for neural networks. However, I believe this package helps beginners to implement neural networks in a low-level fashion. Hence, the package might still be useful for pedagogical purposes.
    • By “feed-foward,” I mean that all other structures (e.g., recurrent, recursive, convolutional) are unrolled to a feed-forward net. Also included is an example with LSTM-based network for question classification.
  • Resources for deep learning: papers, articles, courses: GitHub
  • psyyz10/ This document summarizes some potentially useful papers and code repositories on Sentiment analysis / document classification: GitHubGist
  • char-rnn: This code implements multi-layer Recurrent Neural Network (RNN, LSTM, and GRU) for training/sampling from character-level language models: GitHub
  • Sentiment Analysis with LSTMs: GitHub
  • Keras examples directory: GitHub
  • Stock Market prediction using news headlines by Joshua van Kleef, Valerie Scholten and Emiel Stoelinga: EmielStoelinga/CCMLWI
  • Stock_Market_Prediction: GitHub
  • Stock Predictor and Portfolio Optimizer: GitHub
  • Financial Portfolio Optimization: GitHub




Codes & Packages & API

  • Sentiment140 by by Alec Go, Richa Bhayani, and Lei Huang: Homepage
    • Sentiment140 allows you to discover the sentiment of a brand, product, or topic on Twitter
  • sentiment viz - Tweet Sentiment Visualization
  • NLP at Cornell: Movie Review Data by Bo Pang or Lillian Lee: Homepage
  • Stanford Deeply Moving: Deep Learning for Sentiment Analysis: GitHub, Paper
  • SentiBank: Visual Sentiment Ontology: Homepage, Paper
  • Lin, C., & He, Y. (2009, November). Joint sentiment/topic model for sentiment analysis. In Proceedings of the 18th ACM conference on Information and knowledge management (pp. 375-384). ACM.: GitHub, Paper
  • Mesnil, G., Mikolov, T., Ranzato, M. A., & Bengio, Y. (2014). Ensemble of generative and discriminative techniques for sentiment analysis of movie reviews. arXiv preprint arXiv:1412.5335.: GitHub, Paper
  • Hagen, M., Potthast, M., Büchner, M., & Stein, B. (2015, March). Twitter sentiment detection via ensemble classification using averaged confidence scores. In European Conference on Information Retrieval (pp. 741-754). Springer, Cham.: GitHub, Paper
  • A comparison of open source tools for sentiment analysis: Page, GitHub
  • Using Structured Events to Predict Stock Price Movement: An Empirical Investigation: GitHub, Paper

Document level/sentence level:


  • A convolutional neural network for modelling sentences: GitHub
  • Convolutional neural networks for sentence classification: GitHub
  • Character-level Convolutional Networks for Text Classification: GitHub
  • Dependency Sensitive Convolutional Neural Networks for Modeling Sentences and Documents: GitHub
  • Dependency-based Convolutional Neural Networks for Sentence Embedding: GitHub
  • Discriminative Neural Sentence Modeling by Tree-Based Convolution
  • Multichannel variable-size convolution for sentence classification.


  • Deep recursive neural networks for compositionality in language: Homepage, GitHub|deep-recursive
  • Improved semantic representations from tree- structured long short-term memory networks
  • Document modeling with gated recurrent neural network for sentiment classification: Paper, Videos
  • Hierarchical Attention Networks for Document Classification: Github
  • Semi-supervised Variational Autoencoders for Text Classification_aaai
  • Semi-supervised Sequence Learning
  • Yu, A. W., Lee, H., & Le, Q. V. (2017). Learning to skim text. arXiv preprint arXiv:1704.06877.: [Code]
  • Neural Sentiment Classification with User and Product Attention: GitHub, Paper


  • Deep Unordered Composition Rivals Syntactic Methods for Text Classification
  • Bag of Tricks for Efficient Text Classification


  • Adversarial Multi-task Learning for Text Classification
  • Harnessing Deep Neural Networks with Logic Rules

Aspect level:

  • Aspect-augmented Adversarial Networks for Domain Adaptation: GitHub
  • Aspect Level Sentiment Classification with Deep Memory Network: GitHub
  • Aspect-Based Sentiment Analysis using Tree-Structured LSTMs: GitHub
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