Comments-attached chinese microblog sentiment classification based on machine learning technology

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

Nowadays, with the rapid development of social networks, community-oriented Web sentiment analysis technology has gradually become a hot topic in the field of data mining. Being concise and flexible, Chinese microblog poses new challenges for sentiment analysis. This paper proposes an approach to classify Chinese microblog sentiments into positive and negative by the plain Naive Bayes (NB) and Support Vector Machine (SVM). Based on data preprocessing, sentiment lexicon construction, combining element of users' comments, this research posit this Comments-attached Microblog Sentiment Classification, which is a novel method of attaching microblog users' comments to the target microblog in order to improve the accuracy of sentiment classification. The experiment proves the vitality of this method and the advancement of the indecency from the way of language expressions.

Original languageEnglish
Title of host publicationIntelligent Computing Methodologies - 10th International Conference, ICIC 2014, Proceedings
PublisherSpringer Verlag
Pages173-184
Number of pages12
ISBN (Print)9783319093383
DOIs
Publication statusPublished - 2014
Event10th International Conference on Intelligent Computing, ICIC 2014 - Taiyuan, China
Duration: 3 Aug 20146 Aug 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8589 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th International Conference on Intelligent Computing, ICIC 2014
Country/TerritoryChina
CityTaiyuan
Period3/08/146/08/14

Keywords

  • Chinese Microblog
  • machine learning
  • sentiment classification
  • user's comments

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Yan, B., Zhang, B., Su, H., & Zheng, H. (2014). Comments-attached chinese microblog sentiment classification based on machine learning technology. In Intelligent Computing Methodologies - 10th International Conference, ICIC 2014, Proceedings (pp. 173-184). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8589 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-09339-0_18