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Open AccessArticle

Feature Weighting Based on Inter-Category and Intra-Category Strength for Twitter Sentiment Analysis

by 1 and 2,*
1
College of Information and Communication Engineering, Sungkyunkwan University, Suwon 440-746, Korea
2
College of Software, Sungkyunkwan University, Suwon 440-746, Korea
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(1), 92; https://doi.org/10.3390/app9010092
Received: 3 December 2018 / Revised: 19 December 2018 / Accepted: 22 December 2018 / Published: 27 December 2018
(This article belongs to the Section Computing and Artificial Intelligence)
The rapid growth in social networking services has led to the generation of a massive
volume of opinionated information in the form of electronic text. As a result, the research on text
sentiment analysis has drawn a great deal of interest. In this paper a novel feature weighting approach
is proposed for the sentiment analysis of Twitter data. It properly measures the relative significance
of each feature regarding both intra-category and intra-category distribution. A new statistical model
called Category Discriminative Strength is introduced to characterize the discriminability of the
features among various categories, and a modified Chi-square (2)-based measure is employed to
measure the intra-category dependency of the features. Moreover, a fine-grained feature clustering
strategy is proposed to maximize the accuracy of the analysis. Extensive experiments demonstrate that
the proposed approach significantly outperforms four state-of-the-art sentiment analysis techniques
in terms of accuracy, precision, recall, and F1 measure with various sizes and patterns of training and
test datasets. View Full-Text
Keywords: Twitter sentiment analysis; feature weighting; opinion mining; category distribution Twitter sentiment analysis; feature weighting; opinion mining; category distribution
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MDPI and ACS Style

Wang, Y.; Youn, H.Y. Feature Weighting Based on Inter-Category and Intra-Category Strength for Twitter Sentiment Analysis. Appl. Sci. 2019, 9, 92.

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