Comparative Study of Classification Algorithms in Sentiment Analysis

Authors(2) :-N. Lokeswari, K. Amaravathi

In daily life customer reviews or opinions play a very key role. Whenever anyone has to take a decision, they are likely to consider opinions of others. Analyzing the peopleís feeling is significant for many applications such as companies, ventures, enterprises trying to find out the review of their goods in the marketplace, predicting socio-economic situations like stock exchange and predicting political elections. Since there is a good quantity of collective data present on the internet automatically therefore, itís very important to employ techniques that automatically classify them. Sentiment Classification is also called as Opinion Mining. This project addresses sentiment analysis in Hotel reviews that classifies the hotel reviews according to the sentiment expressed by the public into: positive or negative. To classify the hotel reviews i.e. the given sentiment we used natural language tool kit (NLTK) to pre-process the natural language tasks. While pre-processing the text review first we remove stop words, punctuations and regular expressions .We also used n-grams to improve the sentimental analysis. After pre-processing the sentiment we choose a classifier to classify the sentiment. So, in order to selecting the best classifier in this paper we use different classifiers of different models to classify the sentiment and we also give the brief comparison of classifiers in terms of Accuracy, Precision, Recall and F-Measure. With these evaluation metrics we can show the best classifier. Multinomial NaiveBayes is the most accurate classifier to our data set and it gives quite efficient results than other classifiers.

Authors and Affiliations

N. Lokeswari
CSE, Anil Neerukonda Institute of Technology and Sciences, Visakhapatnam, Andhra Pradesh, India
K. Amaravathi
CSE, Anil Neerukonda Institute of Technology and Sciences, Visakhapatnam, Andhra Pradesh, India

Natural language tool kit (NLTK), Multinomial NaiveBayes, Accuracy, Precision, Recall and F-Measure, n-grams, classifier.

  1. H. Saif, M. Fernandez, Y. He, and H. Alani, "On Stopwords, Filtering and Data Sparsity for Sentiment Analysis of Twitter," Proc. Ninth Int. Conf. Lang. Resour.Eval., no. i, pp. 810–817, 2014.
  2. "Python programming." Online]. Available: https://pythonprogramming.net/.
  3. "Machine Learning Tutorials." Online]. Available: http://machinelearningmastery.com/.
  4. F. Pedregosa, R. Weiss, and M. Brucher, "Scikit-learn?: Machine Learning in Python," vol. 12, pp. 2825–2830, 2011.
  5. G. Vinodhini and R. Chandrasekaran, "Sentiment Analysis and Opinion Mining: A Survey," Int. J. Adv. Res. Comput. Sci. Softw. Eng., vol. 2, no. 6, pp. 282–292, 2012.
  6. V. Narayanan, I. Arora, and a Bhatia, "Fast and accurate sentiment classification using an enhanced Naive Bayes model," Int. Data Eng. Autom. Learn. Lect. Notes Comput.Sci., vol. 8206, pp. 194–201, 2013.
  7. R. S. Michalski, J. G. Carbonell, and T. M. Mitchell,"Machine Learning: An Artificial Intelligence Approach," vol. 2, no. 12, pp. 3400–3405, 1984.
  8. W. Pan, X. Shen, and B. Liu, "Cluster Analysis: Unsupervised Learning via Supervised Learning with a Non-convex Penalty.," J. Mach. Learn. Res., vol. 14, no. 7, p. 1865, 2013.

Publication Details

Published in : Volume 4 | Issue 8 | May-June 2018
Date of Publication : 2018-06-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 31-39
Manuscript Number : IJSRSET162220
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

Cite This Article :

N. Lokeswari, K. Amaravathi, " Comparative Study of Classification Algorithms in Sentiment Analysis, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 4, Issue 8, pp.31-39, May-June.2018
URL : http://ijsrset.com/IJSRSET162220

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