IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0165560.html
   My bibliography  Save this article

Determining Fuzzy Membership for Sentiment Classification: A Three-Layer Sentiment Propagation Model

Author

Listed:
  • Chuanjun Zhao
  • Suge Wang
  • Deyu Li

Abstract

Enormous quantities of review documents exist in forums, blogs, twitter accounts, and shopping web sites. Analysis of the sentiment information hidden in these review documents is very useful for consumers and manufacturers. The sentiment orientation and sentiment intensity of a review can be described in more detail by using a sentiment score than by using bipolar sentiment polarity. Existing methods for calculating review sentiment scores frequently use a sentiment lexicon or the locations of features in a sentence, a paragraph, and a document. In order to achieve more accurate sentiment scores of review documents, a three-layer sentiment propagation model (TLSPM) is proposed that uses three kinds of interrelations, those among documents, topics, and words. First, we use nine relationship pairwise matrices between documents, topics, and words. In TLSPM, we suppose that sentiment neighbors tend to have the same sentiment polarity and similar sentiment intensity in the sentiment propagation network. Then, we implement the sentiment propagation processes among the documents, topics, and words in turn. Finally, we can obtain the steady sentiment scores of documents by a continuous iteration process. Intuition might suggest that documents with strong sentiment intensity make larger contributions to classification than those with weak sentiment intensity. Therefore, we use the fuzzy membership of documents obtained by TLSPM as the weight of the text to train a fuzzy support vector machine model (FSVM). As compared with a support vector machine (SVM) and four other fuzzy membership determination methods, the results show that FSVM trained with TLSPM can enhance the effectiveness of sentiment classification. In addition, FSVM trained with TLSPM can reduce the mean square error (MSE) on seven sentiment rating prediction data sets.

Suggested Citation

  • Chuanjun Zhao & Suge Wang & Deyu Li, 2016. "Determining Fuzzy Membership for Sentiment Classification: A Three-Layer Sentiment Propagation Model," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-32, November.
  • Handle: RePEc:plo:pone00:0165560
    DOI: 10.1371/journal.pone.0165560
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0165560
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0165560&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0165560?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Shields, Michael D. & Teferra, Kirubel & Hapij, Adam & Daddazio, Raymond P., 2015. "Refined Stratified Sampling for efficient Monte Carlo based uncertainty quantification," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 310-325.
    2. Gabriele Ranco & Darko Aleksovski & Guido Caldarelli & Miha Grčar & Igor Mozetič, 2015. "The Effects of Twitter Sentiment on Stock Price Returns," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-21, September.
    3. Liu, X. & Murata, T., 2010. "Advanced modularity-specialized label propagation algorithm for detecting communities in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(7), pages 1493-1500.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chiron, Marie & Genest, Christian & Morio, Jérôme & Dubreuil, Sylvain, 2023. "Failure probability estimation through high-dimensional elliptical distribution modeling with multiple importance sampling," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    2. Yousaf, Imran & Youssef, Manel & Goodell, John W., 2022. "Quantile connectedness between sentiment and financial markets: Evidence from the S&P 500 twitter sentiment index," International Review of Financial Analysis, Elsevier, vol. 83(C).
    3. Shang, Ronghua & Zhang, Weitong & Jiao, Licheng & Stolkin, Rustam & Xue, Yu, 2017. "A community integration strategy based on an improved modularity density increment for large-scale networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 471-485.
    4. Matteo Iacopini & Carlo R.M.A. Santagiustina, 2021. "Filtering the intensity of public concern from social media count data with jumps," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(4), pages 1283-1302, October.
    5. Darko Cherepnalkoski & Andreas Karpf & Igor Mozetič & Miha Grčar, 2016. "Cohesion and Coalition Formation in the European Parliament: Roll-Call Votes and Twitter Activities," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-27, November.
    6. Thomas Renault, 2020. "Sentiment analysis and machine learning in finance: a comparison of methods and models on one million messages," Digital Finance, Springer, vol. 2(1), pages 1-13, September.
    7. Marlene Amstad & Leonardo Gambacorta & Chao He & Dora Xia, 2021. "Trade sentiment and the stock market: new evidence based on big data textual analysis of Chinese media," BIS Working Papers 917, Bank for International Settlements.
    8. Lin, Zhen & Zheng, Xiaolin & Xin, Nan & Chen, Deren, 2014. "CK-LPA: Efficient community detection algorithm based on label propagation with community kernel," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 386-399.
    9. Paola Cerchiello & Giancarlo Nicola, 2018. "Assessing News Contagion in Finance," Econometrics, MDPI, vol. 6(1), pages 1-19, February.
    10. Igor Mozetič & Miha Grčar & Jasmina Smailović, 2016. "Multilingual Twitter Sentiment Classification: The Role of Human Annotators," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-26, May.
    11. Li, Wei & Huang, Ce & Wang, Miao & Chen, Xi, 2017. "Stepping community detection algorithm based on label propagation and similarity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 472(C), pages 145-155.
    12. Rizman Žalik, Krista & Žalik, Borut, 2014. "A local multiresolution algorithm for detecting communities of unbalanced structures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 407(C), pages 380-393.
    13. Agrrawal, Pankaj & Agarwal, Rajat, 2023. "A Longer-Term evaluation of Information releases by Influential market Agents and the Semi-strong market Efficiency," EconStor Preprints 273555, ZBW - Leibniz Information Centre for Economics.
    14. Ahelegbey, Daniel Felix & Cerchiello, Paola & Scaramozzino, Roberta, 2022. "Network based evidence of the financial impact of Covid-19 pandemic," International Review of Financial Analysis, Elsevier, vol. 81(C).
    15. Soudeep Deb, 2023. "Analyzing airlines stock price volatility during COVID‐19 pandemic through internet search data," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(2), pages 1497-1513, April.
    16. Arcuri, Maria Cristina & Gandolfi, Gino & Russo, Ivan, 2023. "Does fake news impact stock returns? Evidence from US and EU stock markets," Journal of Economics and Business, Elsevier, vol. 125.
    17. Jimei Shen & Zhehu Yuan & Yifan Jin, 2022. "AlphaMLDigger: A Novel Machine Learning Solution to Explore Excess Return on Investment," Papers 2206.11072, arXiv.org, revised Dec 2022.
    18. Wang, Zuxi & Li, Qingguang & Xiong, Wei & Jin, Fengdong & Wu, Yao, 2016. "Fast community detection based on sector edge aggregation metric model in hyperbolic space," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 452(C), pages 178-191.
    19. Thalles Vitelli Garcez & Helder Tenório Cavalcanti & Adiel Teixeira de Almeida, 2021. "A hybrid decision support model using Grey Relational Analysis and the Additive-Veto Model for solving multicriteria decision-making problems: an approach to supplier selection," Annals of Operations Research, Springer, vol. 304(1), pages 199-231, September.
    20. Chen, Long & Huang, Jiahui & Jing, Peng & Wang, Bichen & Yu, Xiaozhou & Zha, Ye & Jiang, Chengxi, 2023. "Changing or unchanging Chinese attitudes toward ride-hailing? A social media analytics perspective from 2018 to 2021," Transportation Research Part A: Policy and Practice, Elsevier, vol. 178(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0165560. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.