Sentiment classification of short text using sentimental context

Wenjie ZHENG, Zenan XU, Yanghui RAO, Haoran XIE, Fu Lee WANG, Reggie KWAN

Research output: Chapter in Book/Report/Conference proceedingChapters

Abstract

Sentiment analysis has important applications in many areas, including marketing, recommendation, and financial analysis. Since topic modeling can discover hidden semantic structures, researchers put forward sentiment analysis models based on topic models. These models have been successfully applied on long texts, but analysis for short text is a challenging task because of the sparsity of features in short texts. We observe that the textual context has been widely considered on text analysis task, but on sentiment analysis area, most sentiment analysis models still lack of consideration and integration of sentimental context. Thus, by taking the speciality of sentiment analysis task and short text into consideration, we propose the sentimental context to enrich the characteristics and improve the performance of sentiment classification over short text. We first put forward the concept of sentimental context, which is extracted from the text body and sentiment lexicon, and then we integrate the sentimental context and propose two sentiment classification models based on word-level and topic-level respectively. We present results on real-world datasets from various sources, validating the effectiveness of the proposed models. Copyright © 2017 IEEE.
Original languageEnglish
Title of host publication2017 international conference on behavioral, economic, and socio-cultural computing (BESC 2017): Proceedings
Place of PublicationDanvers, MA
PublisherIEEE
Pages211-216
ISBN (Print)9781538623657, 9781538623671
DOIs
Publication statusPublished - 2018

Citation

Zheng, W., Xu, Z., Rao, Y., Xie, H., Wang, F. L., & Kwan, R. (2017). Sentiment classification of short text using sentimental context. In 2017 international conference on behavioral, economic, and socio-cultural computing (BESC 2017): Proceedings (pp. 211-216). Danvers, MA: IEEE.

Keywords

  • Context modeling
  • Analytical models
  • Sentiment analysis
  • Text analysis
  • Training
  • Semantics

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