Sentimental Spidering: Leveraging Opinion Information in Focused Crawlers
Fu, Tianjun2; Abbasi, Ahmed3; Zeng, Daniel1; Chen, Hsinchun4
AbstractDespite the increased prevalence of sentiment-related information on the Web, there has been limited work on focused crawlers capable of effectively collecting not only topic-relevant but also sentiment-relevant content. In this article, we propose a novel focused crawler that incorporates topic and sentiment information as well as a graph-based tunneling mechanism for enhanced collection of opinion-rich Web content regarding a particular topic. The graph-based sentiment (GBS) crawler uses a text classifier that employs both topic and sentiment categorization modules to assess the relevance of candidate pages. This information is also used to label nodes in web graphs that are employed by the tunneling mechanism to improve collection recall. Experimental results on two test beds revealed that GBS was able to provide better precision and recall than seven comparison crawlers. Moreover, GBS was able to collect a large proportion of the relevant content after traversing far fewer pages than comparison methods. GBS outperformed comparison methods on various categories of Web pages in the test beds, including collection of blogs, Web forums, and social networking Web site content. Further analysis revealed that both the sentiment classification module and graph-based tunneling mechanism played an integral role in the overall effectiveness of the GBS crawler.
KeywordAlgorithms Experimentation Design Performance Web Crawlers Focused Crawlers Sentiment Analysis Opinion Mining Classification Graph Similarities Random Walk Path
WOS HeadingsScience & Technology ; Technology
Indexed BySCI
WOS Research AreaComputer Science
WOS SubjectComputer Science, Information Systems
WOS IDWOS:000312428900005
Citation statistics
Cited Times:28[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Affiliation1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.Google Inc, Mountain View, CA 94043 USA
3.Univ Virginia, Informat Technol Area, Charlottesville, VA 22904 USA
4.Univ Arizona, Dept Management Informat Syst, Tucson, AZ 85721 USA
Recommended Citation
GB/T 7714
Fu, Tianjun,Abbasi, Ahmed,Zeng, Daniel,et al. Sentimental Spidering: Leveraging Opinion Information in Focused Crawlers[J]. ACM TRANSACTIONS ON INFORMATION SYSTEMS,2012,30(4).
APA Fu, Tianjun,Abbasi, Ahmed,Zeng, Daniel,&Chen, Hsinchun.(2012).Sentimental Spidering: Leveraging Opinion Information in Focused Crawlers.ACM TRANSACTIONS ON INFORMATION SYSTEMS,30(4).
MLA Fu, Tianjun,et al."Sentimental Spidering: Leveraging Opinion Information in Focused Crawlers".ACM TRANSACTIONS ON INFORMATION SYSTEMS 30.4(2012).
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