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Co-training and visualizing sentiment evolvement for tweet events

Published: 13 May 2013 Publication History

Abstract

Sentiment classification on tweet events attracts more interest in recent years. The large tweet stream stops people reading the whole classified list to understand the insights. We employ the co-training framework in the proposed algorithm. Features are split into text view features and non-text view features. Two Random Forest (RF) classifiers are trained with the common labeled data on the two views of features separately. Then for each specific event, they collaboratively and periodically train together to boost the classification performance. At last, we propose a "river" graph to visualize the intensity and evolvement of sentiment on an event, which demonstrates the intensity by both color gradient and opinion labels, and the ups and downs of confronting opinions by the river flow. Comparing with the well-known sentiment classifiers, our algorithm achieves consistent increases in accuracy on the tweet events from TREC 2011 Microblogging and our database. The visualization helps people recognize turning and bursting patterns, and predict sentiment trend in an intuitive way.

References

[1]
Godbole, N., Srinivasaiah, M., Skiena, S. 2007. Large Scale Sentiment Analysis for News and Blogs. In Proc. of CWSM'2007.
[2]
Thelwall, M., Buckley, K. and Paltoglou, G. 2011. Sentiment in Twitter events. Journal of the American Society for Information Science and Technology. Volume 62, Issue 2. pp. 406--418
[3]
Nguyen, L. T, Wu, P., Chan, W., Peng, W., Zhang, Y. 2012. Predicting Collective Sentiment Dynamics from Time-series Social Media. In Proc. of WISDOM. p.6.
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Wu, Y., Wei, F., Liu, S., Au, N., Cui, W., Zhou, H. and Qu, H. 2010. OpinionSeer: Interactive Visualization of Hotel Customer Feedback, IEEE Trans. on VCG, Vol. 16, No. 6, pp. 1109--1118.
[5]
Hao, M., Rohrdantz, C., Janetzko, H., Dayal, U. 2011. Visual Sentiment Analysis on Twitter Data Streams. IEEE Symposium on Visual Analytics Science and Technology, pages 275--276.
[6]
Blum, A. and Mitchell, T. 1998.Combining labeled and unlabeled data with co-training. In Proc. of COLT. pp. 92--100.
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Turney, P. D. 2002. Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews. In Proc. of ACL. pp. 417--424
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Strapparava, C. and Valitutti, A. 2004. Wordnet-affect: an affective extension of wordnet. In Proc. of LREC. pp. 1083--1086.

Cited By

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  • (2024)Decoding COVID-19 Conversations with Visualization: Twitter Analytics and Emerging TrendsJournal of Computer Science Engineering and Software Testing10.46610/JOCSES.2024.v10i01.00310:1(21-31)Online publication date: 2024
  • (2022)An Interactive Visualization Tool to Explore People’s Tweets towards COVID-19Proceedings of the 2022 International Conference on Advanced Visual Interfaces10.1145/3531073.3534496(1-3)Online publication date: 6-Jun-2022
  • (2022)Sentiment Analysis of Public Social Media as a Tool for Health-Related TopicsIEEE Access10.1109/ACCESS.2022.318740610(74850-74872)Online publication date: 2022
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    Published In

    cover image ACM Other conferences
    WWW '13 Companion: Proceedings of the 22nd International Conference on World Wide Web
    May 2013
    1636 pages
    ISBN:9781450320382
    DOI:10.1145/2487788
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    • NICBR: Nucleo de Informatcao e Coordenacao do Ponto BR
    • CGIBR: Comite Gestor da Internet no Brazil

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 13 May 2013

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    Author Tags

    1. co-training
    2. microblog events
    3. sentiment analysis
    4. visualization

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    WWW '13
    Sponsor:
    • NICBR
    • CGIBR
    WWW '13: 22nd International World Wide Web Conference
    May 13 - 17, 2013
    Rio de Janeiro, Brazil

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    WWW '13 Companion Paper Acceptance Rate 831 of 1,250 submissions, 66%;
    Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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    Cited By

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    • (2024)Decoding COVID-19 Conversations with Visualization: Twitter Analytics and Emerging TrendsJournal of Computer Science Engineering and Software Testing10.46610/JOCSES.2024.v10i01.00310:1(21-31)Online publication date: 2024
    • (2022)An Interactive Visualization Tool to Explore People’s Tweets towards COVID-19Proceedings of the 2022 International Conference on Advanced Visual Interfaces10.1145/3531073.3534496(1-3)Online publication date: 6-Jun-2022
    • (2022)Sentiment Analysis of Public Social Media as a Tool for Health-Related TopicsIEEE Access10.1109/ACCESS.2022.318740610(74850-74872)Online publication date: 2022
    • (2022)A survey on classification techniques for opinion mining and sentiment analysisArtificial Intelligence Review10.1007/s10462-017-9599-652:3(1495-1545)Online publication date: 10-Mar-2022
    • (2021)TEVisE: An Interactive Visual Analytics Tool to Explore Evolution of Keywords’ Relations in Tweet DataHuman-Computer Interaction – INTERACT 202110.1007/978-3-030-85613-7_37(579-599)Online publication date: 30-Aug-2021
    • (2020)Weibo Sentiment Classification Based on Two Channels Text Convolution Neural Network with Multi-Feature2020 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)10.1109/CyberC49757.2020.00033(152-160)Online publication date: Oct-2020
    • (2019)Twitter mining using semi-supervised classification for relevance filtering in syndromic surveillancePLOS ONE10.1371/journal.pone.021068914:7(e0210689)Online publication date: 18-Jul-2019
    • (2019)A Survey of Opinion Mining in ArabicACM Transactions on Asian and Low-Resource Language Information Processing10.1145/329566218:3(1-52)Online publication date: 7-May-2019
    • (2019)Bridging Text Visualization and Mining: A Task-Driven SurveyIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2018.283434125:7(2482-2504)Online publication date: 1-Jul-2019
    • (2019)Sentiment analysis on big sparse data streams with limited labelsKnowledge and Information Systems10.1007/s10115-019-01392-962:4(1393-1432)Online publication date: 17-Aug-2019
    • Show More Cited By

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