Open AccessUse of NLP Based Combined Features for Sentiment Classification
Author(s) -
K. Kalaivani,
Prof.S. Kuppuswami
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.f8290.109119
Subject(s) - bigram , trigram , computer science , artificial intelligence , naive bayes classifier , sentiment analysis , natural language processing , feature selection , classifier (uml) , machine learning , pattern recognition (psychology) , support vector machine
Sentiment analysis is the technique of automatic detection of the belief or the mood of an author towards a certain subject in textual form. To extract the opinion present in text, the machine needs expertise in the area of natural language processing. In this paper, machine learning based document-level sentiment classification is performed on Amazon product reviews to classify them as positive and negative. Two NLP based feature extraction techniques (Word Relation and POS based) are used in this study to determine the features that are sentiment bearing. The features are extracted as basic features (unigrams, bigrams and trigrams) and their combinations (unigrams+bigrams, unigrams+trigrams, unigrams+bigrams+trigrams). In order to identify the features that are most informative and to bring down the computational time of the classification algorithms, feature selection techniques are used. Performance of independent and combined feature sets is assessed using accuracy, precision, recall and F-measure. From the experiments conducted, it is observed that combined features outperformed independent features using Boolean Multinomial Naive Bayes (BMNB) classifier.