Using Lexicon and Random Forest Classifier for Twitter Sentiment Analysis

Authors

  • M Thenmozhi Dept of Computer Science and Engineering, Pondicherry Engineering College, Puducherry, India
  • R Indira Dept of Computer Science and Engineering, Pondicherry Engineering College, Puducherry, India
  • R Dharani Dept of Computer Science and Engineering, Pondicherry Engineering College, Puducherry, India

DOI:

https://doi.org/10.26438/ijcse/v7i6.591594

Keywords:

Sentiment Analysis, Sentiment Classification, Lexicon based Analysis, Sentiment Score

Abstract

Today users prefer blogs and review sites to purchase products online. Thus, user reviews are considered as an important source of information in sentiment analysis applications for decision making. Machine Learning and Lexicon based sentiment analysis are the two popular methods available in the literature. The Machine Learning based classifiers does not work for unlabelled dataset such as tweets. On the other hand existing Lexicon based sentiment analysis approaches are becoming less efficient due to data sparseness, low accuracy and non-consideration n-gram words. N-grams can improve the accuracy of sentiment classification. Following these limitations the proposed work provides a combination of Lexicon and Machine learning based approach to perform sentiment analysis on Twitter datasets.

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Published

2019-06-30
CITATION
DOI: 10.26438/ijcse/v7i6.591594
Published: 2019-06-30

How to Cite

[1]
M. Thenmozhi, I. R, and R. Dharani, “Using Lexicon and Random Forest Classifier for Twitter Sentiment Analysis”, Int. J. Comp. Sci. Eng., vol. 7, no. 6, pp. 591–594, Jun. 2019.

Issue

Vol. 7 No. 6 (2019): IJCSE June Edition

Section

Research Article

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