Authors: B. Madhusudhanan; S. Chitra; S. Anbuchelian
Addresses: Department of Computer Science, Er.Perumal Manimekalai College of Engineering, Tamilnadu, India ' Department of Computer Science, Er.Perumal Manimekalai College of Engineering, Tamilnadu, India ' Ramanujan Computing Center, CEG Campus, Anna University, Chennai, 600 025, India
Abstract: Recently, a lot of attention paid to the domain of sentiment analysis (SA), with experts acknowledging the scientific trials as well as possible applications of the processing of subjective language. SA is the computational analysis of opinions or sentiments conveyed in a body of text. The aim of SA is the detection of subjective data present in several sources and figure out the attitudes of the author regarding the topic. Features extraction looks after the identification of the features used for opinion mining, features selection is used for choosing best features for opinion classification, features weighting method is used for weighting features for good recommendations, reduction methods are used for optimisation of classification procedure. In the current study, the feature extraction is carried out term frequency/inverse document frequency and features selection through conditional mutual information maximisation (CMIM). Feature classification is done through LogitBoost, chi-squared automatic interaction detector (CHAID) as well as K-nearest neighbour (KNN) classifiers. The experimental results were contrasted with one another.
Keywords: sentiment analysis; LogitBoost; CHAID; CMIM; K-nearest neighbor; KNN; term frequency/inverse document frequency; stemming; stop words.
International Journal of Advanced Intelligence Paradigms, 2019 Vol.13 No.3/4, pp.368 - 381
Received: 09 Dec 2016
Accepted: 18 Feb 2017
Published online: 03 Sep 2019 *