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Sentiment Analysis with Naive Bayes and LSTM

In this notebook, we try to predict the positive (label 1) or negative (label 0) sentiment of the sentence. We use the UCI Sentiment Labelled Sentences Data Set.

Sentiment analysis is very useful in many areas. For example, it can be used for internet conversations moderation. Also, it is possible to predict ratings that users can assign to a certain product (food, household appliances, hotels, films, etc) based on the reviews.

In this notebook we are using two families of machine learning algorithms: Naive Bayes (NB) and long short term memory (LSTM) neural networks.


  • Deeplearning4j

  • Understanding LSTM Networks

  • Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling  

  • The Unreasonable Effectiveness of Recurrent Neural Networks

We will use pandas, numpy for data manipulation, nltk for natural language processing, matplotlib, seaborn and plotly for data visualization, sklearn and keras for learning the models.

Read the full article with source code and illustrations, here

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