Sentiment analysis of book reviews using CNN with n-grams method
Addanki Mounika, S. Saraswathi · International Journal of Knowledge Engineering and Data Mining · 2021
The fundamental job of sentiment analysis (SA) is to decide the sentiment polarity (positive or negative) of the text. It is a problematic task to take the sentiments in the document level sentences accurately. In the proposed system we developed sentiment analysis of book reviews using CNN with n-grams method by utilising two levels. In the first level, 'grouping of similar tagged words by semantic network' is completed taking pre-processed data utilising parts of speech (POS) Tagger from the datasets of books and reviewers by WuPalmer word similarity technique. In the second level, 'SA' is completed in two stages which are the training phase and testing phase by utilising deep learning approaches like convolutional neural networks (CNN) with n-gram method using document to vector (Doc2Vec) and distributed bag of words (DBOW) embedding. The proposed system 'CNN+Doc2Vec+DBOW+n-gram' divides the book reviews into positive or negative reviews with better accuracy results compared to existing methods.