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Presenter : Wu, Min-Cong Authors : Yung-Ming Li, Tsung -Ying Li 2013, DSS

Deriving market intelligence from microblogs. Presenter : Wu, Min-Cong Authors : Yung-Ming Li, Tsung -Ying Li 2013, DSS. Outlines. Motivation Objectives Methodology Experiments Conclusions Comments. Motivation.

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Presenter : Wu, Min-Cong Authors : Yung-Ming Li, Tsung -Ying Li 2013, DSS

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  1. Deriving market intelligence from microblogs Presenter : Wu, Min-Cong Authors : Yung-Ming Li, Tsung-Ying Li2013, DSS

  2. Outlines • Motivation • Objectives • Methodology • Experiments • Conclusions • Comments

  3. Motivation • With more and more customers expressing their opinions on brands and products via social media platforms such as microblogs, it is increasingly important to discover and trace useful insights from microblog platforms

  4. Objectives • we proposed a system designed to summarize text opinions into traceable numeric scores in which users are interested.

  5. Methodology-framework Query:Google Topic:Gmail,Google map…..

  6. Methodology-Trendy topics detection module “iPhone 4” (query) has any problems on “antenna”(topic). a post: “Battery ofiPhone is not good.” matches meronym pattern “PART ofENTITY”.

  7. Methodology- Opinion classification module Subjectivity analysis module: Subjective opinions Objective opinions opinions Φ= the subjective word set from WordNet one word Sentiment classification module: -1:”:)” 1:”:(”

  8. Methodology- Credibility assessment module source credibility score of user I : Reduce interference of spammer threshold of followers/followeesand number of the follweesset to be 10. user's follower–followeeratio content credibility score of user i in a time period TP : repost frequency the geometric mean of source credibility score : threshold of set to be 100. To prevent the improper abuse of credibility

  9. Methodology- Numeric summarization module credibility score of user i semantic score High score: The opinion is more credibility and subjective

  10. Methodology- Data collection, preprocessing, and data description Data collection : period #1 was from 2010/03/05 to 2010/03/25 (20 days). period #2 was from 2010/05/13 to 2010/05/23 (10 days). A set of queries(three brands and three products): preprocessing : a copy of the opinion text was POS-tagged the social networks of follower and followeerelationships were constructed for further credibility analysis.

  11. Experiment-Topic detection effectiveness evaluation MPP use : If any of these patterns was matched,MPP value will be increased

  12. Experiment-Topic detection effectiveness evaluation hashtag can’t split

  13. Experiment-Topic detection effectiveness evaluation It is still comparable to that of the review articles

  14. Experiment-Effects of Wikipedia Corpus Sizes

  15. Experiment-Score aggregation correctness evaluation Six questionnaires, each of which corresponds to a target query on a five-point Likert scale

  16. Experiment-Score aggregation correctness evaluation >0.05

  17. Experiment- Topic-specific sentimental polarity MAE is only slightly improved

  18. Conclusions • The proposed system allows decision makers to understand market trends by tracking the fluctuation of sentiments on particular topics presented in the proposed numeric summarization format.

  19. Comments • Advantages • effectively discover market intelligence (MI) for supporting decision-makers. • Applications • Market trends.

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