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Pacific-Asia Conference on Knowledge Discovery and Data Mining

PAKDD 2015: Advances in Knowledge Discovery and Data Mining pp 52–63Cite as

Sentiment Analysis on Microblogging by Integrating Text and Image Features

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9078))

Abstract

Most studies about sentiment analysis on microblogging usually focus on the features mining from the text. This paper presents a new sentiment analysis method by combing features from text with features from image. Bigram model is applied in text feature extraction while color and texture information are extracted from images. Considering the sentiment classification, we propose a new neighborhood classier based on the similarity of two instances described by the fusion of text and features. Experimental results show that our proposed method can improve the performance significantly on Sina Weibo data (we collect and label the data). We find that our method can not only increasingly improve the F values of the classification comparing with only used text or images features, but also outperforms the NaiveBayes and SVM classifiers using all features with text and images.

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Author information

Authors and Affiliations

  1. Xianlin Road No.163, Qixia District, Jiangsu Province Nanjing City, China

    Yaowen Zhang, Lin Shang & Xiuyi Jia

Authors
  1. Yaowen Zhang
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  2. Lin Shang
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  3. Xiuyi Jia
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Corresponding author

Correspondence to Lin Shang .

Editor information

Editors and Affiliations

  1. Ho Chi Minh City University of Technology, Ho Chi Minh City, Vietnam

    Tru Cao

  2. Singapore Management University, Singapore, Singapore

    Ee-Peng Lim

  3. Nanjing University, Nanjing, China

    Zhi-Hua Zhou

  4. Japan Advanced Institute of Science and Technology, Nomi City, Japan

    Tu-Bao Ho

  5. The University of Hong Kong, Hong Kong, Hong Kong SAR

    David Cheung

  6. Osaka University, Osaka, Japan

    Hiroshi Motoda

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© 2015 Springer International Publishing Switzerland

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Zhang, Y., Shang, L., Jia, X. (2015). Sentiment Analysis on Microblogging by Integrating Text and Image Features. In: Cao, T., Lim, EP., Zhou, ZH., Ho, TB., Cheung, D., Motoda, H. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2015. Lecture Notes in Computer Science(), vol 9078. Springer, Cham. https://doi.org/10.1007/978-3-319-18032-8_5

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  • DOI: https://doi.org/10.1007/978-3-319-18032-8_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-18031-1

  • Online ISBN: 978-3-319-18032-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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