IDEAS home Printed from https://ideas.repec.org/a/bla/jamist/v58y2007i12p1838-1850.html
   My bibliography  Save this article

Mining opinions from the Web: Beyond relevance retrieval

Author

Listed:
  • Lun‐Wei Ku
  • Hsin‐Hsi Chen

Abstract

Documents discussing public affairs, common themes, interesting products, and so on, are reported and distributed on the Web. Positive and negative opinions embedded in documents are useful references and feedbacks for governments to improve their services, for companies to market their products, and for customers to purchase their objects. Web opinion mining aims to extract, summarize, and track various aspects of subjective information on the Web. Mining subjective information enables traditional information retrieval (IR) systems to retrieve more data from human viewpoints and provide information with finer granularity. Opinion extraction identifies opinion holders, extracts the relevant opinion sentences, and decides their polarities. Opinion summarization recognizes the major events embedded in documents and summarizes the supportive and the nonsupportive evidence. Opinion tracking captures subjective information from various genres and monitors the developments of opinions from spatial and temporal dimensions. To demonstrate and evaluate the proposed opinion mining algorithms, news and bloggers' articles are adopted. Documents in the evaluation corpora are tagged in different granularities from words, sentences to documents. In the experiments, positive and negative sentiment words and their weights are mined on the basis of Chinese word structures. The f‐measure is 73.18% and 63.75% for verbs and nouns, respectively. Utilizing the sentiment words mined together with topical words, we achieve f‐measure 62.16% at the sentence level and 74.37% at the document level.

Suggested Citation

  • Lun‐Wei Ku & Hsin‐Hsi Chen, 2007. "Mining opinions from the Web: Beyond relevance retrieval," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 58(12), pages 1838-1850, October.
    • Handle: RePEc:bla:jamist:v:58:y:2007:i:12:p:1838-1850
    DOI: 10.1002/asi.20630
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/asi.20630
    Download Restriction: no
    File URL: https://libkey.io/10.1002/asi.20630?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
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Xi Zhang & Yunjia Zhang & Senzhang Wang & Yuntao Yao & Binxing Fang & Philip S. Yu, 2018. "Improving Stock Market Prediction via Heterogeneous Information Fusion," Papers 1801.00588, arXiv.org.
    2. Michael D. Wang & Jie Lou & Dong Zhang & C. Simon Fan, 2022. "Measuring political and economic uncertainty: a supervised computational linguistic approach," SN Business & Economics, Springer, vol. 2(5), pages 1-17, May.
    3. Heng Tang & Chang Boon Patrick Lee & Kwee Keong Choong, 2017. "Consumer decision support systems for novice buyers – a design science approach," Information Systems Frontiers, Springer, vol. 19(4), pages 881-897, August.
    4. Heng Tang & Chang Boon Patrick Lee & Kwee Keong Choong, 0. "Consumer decision support systems for novice buyers – a design science approach," Information Systems Frontiers, Springer, vol. 0, pages 1-17.
    5. David Gunnarsson Lorentzen, 2014. "Webometrics benefitting from web mining? An investigation of methods and applications of two research fields," Scientometrics, Springer;Akadémiai Kiadó, vol. 99(2), pages 409-445, May.
    6. Xinlu Li & Yuanyuan Lei & Shengwei Ji, 2022. "BERT- and BiLSTM-Based Sentiment Analysis of Online Chinese Buzzwords," Future Internet, MDPI, vol. 14(11), pages 1-15, November.
    7. Singh, Amit & Jenamani, Mamata & Thakkar, Jitesh J. & Rana, Nripendra P., 2022. "Quantifying the effect of eWOM embedded consumer perceptions on sales: An integrated aspect-level sentiment analysis and panel data modeling approach," Journal of Business Research, Elsevier, vol. 138(C), pages 52-64.
    8. Xin Lu & Donghong Gu & Haolan Zhang & Zhengxin Song & Qianhua Cai & Hongya Zhao & Haiming Wu, 2022. "Semi-Supervised Sentiment Classification on E-Commerce Reviews Using Tripartite Graph and Clustering," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 18(1), pages 1-20, January.
    9. Jia-Lang Xu & Ying-Lin Hsu, 2022. "The Impact of News Sentiment Indicators on Agricultural Product Prices," Computational Economics, Springer;Society for Computational Economics, vol. 59(4), pages 1645-1657, April.
    10. Chuan Zhang & Yu-Xin Tian & Ling-Wei Fan, 2020. "Improving the Bass model’s predictive power through online reviews, search traffic and macroeconomic data," Annals of Operations Research, Springer, vol. 295(2), pages 881-922, December.

    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:bla:jamist:v:58:y:2007:i:12:p:1838-1850. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.asis.org .

    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.