Abstract—Twitter sentiment analysis has been explored in various domains including Business reviews, Political forecasting, decision support, Movie reviews and many more. The nature of data collected by Twitter imposes several challenges for sentiment analysis. There are other factors also like the selected classifier, multiclass sentiment analysis, feature selection method, number of feature selected, level of preprocessing, preprocessing techniques involved that can affect the accuracy of classification. This paper discusses various factors affecting the accuracy of Twitter sentiment analysis. Consideration of these factors can be very beneficial while designing an efficient classification model for twitter sentiment analysis. The survey also focuses on various metrics used for representation of sentiment analysis result and their relevance.
Index Terms—Twitter sentiment analysis, recall, class imbalance, multiclass classification, feature selection.
Sangeeta and Nasib Singh Gill are with Department of Computer Science & Application with Maharishi Dayanand University, Rohtak, India (e-mail: sangeeta.yogi@gmail.com, nasibsgill@gmail.com).
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Cite:Sangeeta and Nasib Singh Gill, "Review of Factors Affecting Efficiency of Twitter Data Sentiment Analysis," International Journal of Computer Theory and Engineering vol. 12, no. 2, pp. 53-58, 2020.
Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
