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We have used 250 sentences of movie reviews available for research from IIT bombay and also crawled and manually annotated 750 reviews from jagran.com, In total 1000 reviews. After preprocessing the dataset, We generate the featureset as a vector-based approach using Term frequency, tfidf for unigrams and bigrams. Then we used three approaches t…

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We have used 250 sentences of movie reviews available for research from IIT bombay and also crawled and manually annotated 750 reviews from jagran.com, In total 1000 reviews. After preprocessing the dataset, We generate the featureset as a vector-based approach using Term frequency, tfidf for unigrams and bigrams. Then we used three approaches t…

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  • Jupyter Notebook 50.1%
  • Python 49.9%