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Sentiment analysis for online reviews using conditional random fields and support vector machines

Author

Listed:
  • Huosong Xia

    (Wuhan Textile University
    Key Research Institute of Humanities and Social Sciences in Universities of Hubei Province)

  • Yitai Yang

    (Wuhan Textile University)

  • Xiaoting Pan

    (Wuhan Textile University)

  • Zuopeng Zhang

    (University of North Florida)

  • Wuyue An

    (Wuhan Textile University)

Abstract

Sentiment analysis of online reviews is an important way of mining useful information from the Internet. Despite several advantages, the accuracy of sentiment analysis based on a domain dictionary relies on the comprehensiveness and accuracy of the dictionary. Instead of creating a domain dictionary, we propose an approach for online review sentiment classification, which uses a conditional random field algorithm to extract the emotional characteristics from fragments of the review. The characteristic (feature) words are then weighted asymmetrically before a support vector machine classifier is used to obtain the sentiment orientation of the review. In our experiments, the average accuracy reached 90%, showing that using sentiment feature fragments instead of whole reviews and weighting the characteristic words asymmetrically can improve the sentiment classification accuracy.

Suggested Citation

  • Huosong Xia & Yitai Yang & Xiaoting Pan & Zuopeng Zhang & Wuyue An, 2020. "Sentiment analysis for online reviews using conditional random fields and support vector machines," Electronic Commerce Research, Springer, vol. 20(2), pages 343-360, June.
    • Handle: RePEc:spr:elcore:v:20:y:2020:i:2:d:10.1007_s10660-019-09354-7
    DOI: 10.1007/s10660-019-09354-7
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    Cited by:

    1. Lingyun Zhai & Pengzhen Yin & Chenyang Li & Jingjing Wang & Min Yang, 2022. "Investigating the Effects of Video-Based E-Word-of-Mouth on Consumers’ Purchase Intention: The Moderating Role of Involvement," Sustainability, MDPI, vol. 14(15), pages 1-19, August.
    2. Shugang Li & Fang Liu & Yuqi Zhang & Boyi Zhu & He Zhu & Zhaoxu Yu, 2022. "Text Mining of User-Generated Content (UGC) for Business Applications in E-Commerce: A Systematic Review," Mathematics, MDPI, vol. 10(19), pages 1-26, September.
    3. Jilong Zhang & Jin Zhang & Kanliang Wang & Wei Yan, 2023. "Should doctors use or avoid medical terms? The influence of medical terms on service quality of E-health," Electronic Commerce Research, Springer, vol. 23(3), pages 1775-1805, September.
    4. Aneeta Elsa Simon & Manu K.S., 2023. "Does Sentiments Impact the Returns of Commodity Derivatives? An Evidence from Multi-commodity Exchange India," Vision, , vol. 27(1), pages 79-92, February.
    5. Xing, Yunfei & Zhang, Justin Zuopeng & Teng, Guangqing & Zhou, Xiaotang, 2024. "Voices in the digital storm: Unraveling online polarization with ChatGPT," Technology in Society, Elsevier, vol. 77(C).
    6. Liang Xiao & Linyong Luo & Tongping Ke, 2024. "The influence of eWOM information structures on consumers’ purchase intentions," Electronic Commerce Research, Springer, vol. 24(3), pages 1713-1735, September.

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