Investigating Machine Learning Techniques for User Sentiment Analysis

Investigating Machine Learning Techniques for User Sentiment Analysis

Nimesh V. Patel (C.U Shah University, Wadhawan (Surendranagar-Gujarat), India) and Hitesh Chhinkaniwala (Adani Institute of Infrastructure Engineering, Gujarat, India)
Copyright: © 2019 |Pages: 12
DOI: 10.4018/IJDSST.2019070101


Sentiment analysis identifies users in the textual reviews available in social networking sites, tweets, blog posts, forums, status updates to share their emotions or reviews and these reviews are to be used by market researchers to do know the product reviews and current trends in the market. The sentiment analysis is performed by two methods. Machine learning approaches and lexicon methods which are also known as the knowledge base approach. These. In this article, the authors evaluate the performance of some machine learning techniques: Maximum Entropy, Naïve Bayes and Support Vector Machines on two benchmark datasets: the positive-negative dataset and a Movie Review dataset by measuring parameters like accuracy, precision, recall and F-score. In this article, the authors present the performance of various sentiment analysis and classification methods by classifying the reviews in binary classes as positive, negative opinion about reviews on different domains of dataset. It is also justified that sentiment analysis using the Support Vector Machine outperforms other machine learning techniques.
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In movie review domain where the application of sentiment analysis has the greatest potential to generate value, it is an interesting and challenging testing ground for different sentiment classification approaches. It was hypothesized that this was due to the tendency of reviewers to rate the individual elements of items differently from the item as a whole within the same review. (Bo Pang et al., 2002) suggest, the machine learning methods and features used when classifying movie reviews do not have to be specific to that domain but its benefits may easily transfer to other areas where sentiment classification can be applied. For binary (positive and negative) sentiment classification, (Turney et al., 2002) proposed the positive and negative terms can be counted and expressions in a review used to determine its polarity. (Kennedy et al., 2006) who had considered negation words, intensifiers, and diminishes taken into account and managed for improving the accuracy of the system. In this article Support Vector Machines (SVMs) method was proposed, and it was inferred that this machine learning algorithm performs significantly better than the term-counting method by considering negation words. (Pang et al., 2002) and (Pang et al., 2004) have compared the performance of various classification algorithms when determining the sentiment of a document, and also proved that SVMs were generally the best approach. Unigrams, bigrams, part-of-speech (POS) tags and term positions were considered as features, and using unigrams gives the best results. When using very simple features with multi-aspect sentiment analysis the SVM classification algorithm can be effective for performing sentiment analysis.

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