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Sentimental Analysis using Asymmetric Least Squares Twin Support Vector Machine (LSTSVM)

N. Saranya, Dr. R. Gunavathi


Sentiment analysis known as opinion mining is the process of determining the emotional tone behind a series of words, used to gain an understanding of the attitudes, opinions and emotions expressed within an online mention.  It is also known as opinion mining used in the voice of the customer materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from marketing to customer service to clinical medicine. This paper works on finding approaches that generate output with good accuracy through (aLSTSVM) Asymmetric Least Squares Twin Support Vector Machine. aLSTSVM is a new version of support vector machine (SVM) based on asymmetric least square and non-parallel twin hyperplanes. It is an efficient fast algorithm for binary classification and its parameters depend on the nature of the problem. A result on several benchmark datasets is applied to train a sentiment classifier in order to demonstrate the accuracy of the proposed algorithm. N-grams and different weighting scheme were used to take out the most classical features. It also analyzes Chi-Square weight features to select informative features for the classification. Experimental analysis reveals that by using Chi-Square feature selection in aLSTSVM may provide significant improvement on classification accuracy.


Chi-Square Weight, Asymmetric Least Squares Support Vector Machine (aLSTSVM), Support Vector Machine (SVM).

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