Indian Journal of Science and Technology
Year: 2017, Volume: 10, Issue: 35, Pages: 1-9
Esha Tyagi* and Arvind Kumar Sharma
*Author for correspondence
Career Point University, Kota – 324005, Rajasthan, India; [email protected]
Now days, there is a huge increase in number of peoples who have been accessing many social networking sites and microblogging websites which open a new door to the impression of today’s generation. Various user reviews for a specific product, company, brand, individual, forums and movies etc. have been much helpful in judging the perception of people. Thus the analysts took the initiative to develop algorithms to automate the classification of distinctive reviews on the basis of their polarities particularly: Positive, and Negative. This automated classification mechanism is referred as Sentiment Analysis. The primary aim of this paper is to apply Support Vector Machine (SVM) machine learning algorithm to classify the sentiments and texts for product reviews that analyses different datasets used for classification of sentiments and texts. Furthermore, various data sets have been utilized for training as well as testing and implementing the Support Vector Machine learning algorithm to find the polarity of the ambiguous sentiments. Objectives: The primary objective of this paper is to implement Support Vector Machine (SVM) machine learning algorithm to classify the sentiments and texts for product reviews that analyses different datasets used for classification of sentiments and texts. Methods/Statistical Analysis: In this research study, several datasets have been applied for training as well as testing and simulating the support vector machine learning algorithm to compute the polarity of the ambiguous sentiments or reviews. Findings: At the outset, Support Vector Machine (SVM) classification algorithm gives higher accuracy 89.98% than the other ones. The obtained accuracy would be enhanced further by including more sentence forms. Finally, it concludes that the Support Vector Machine (SVM) algorithm behaves well. Application/Improvements: The performance resulting models are tested to measure accuracy of Support Vector Machine learning algorithm. Finally, the SVM classification algorithm has been achieved high accuracy and found better classification algorithm than others.
Keywords: Clustering, Product Reviews, SVM, Sentiment Analysis
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