1. Intelligent Database Systems Lab Presenter : WU, MIN-CONG Authors : YUNG-MING LI, TSUNG-YING LI 2013, DSS Deriving market intelligence from microblogs

2. Intelligent Database Systems Lab Outlines Motivation Objectives Methodology Experiments Conclusions Comments

3. Intelligent Database Systems Lab 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. Intelligent Database Systems Lab Objectives we proposed a system designed to summarize text opinions into traceable numeric scores in which users are interested.

5. Intelligent Database Systems Lab Methodology-framework Query:Google Topic:Gmail,Google map…..

6. Intelligent Database Systems Lab Methodology-Trendy topics detection module Q as a set of queries O as the set of opinions the system has collected. q ∈ Q is a query given by end users O q ⊂ O represents a set of opinions in which a query q is mentioned. T is defined as the set of nouns/phrases that appear in opinion set O t ∈ T is a distinct term in T. “iPhone 4” (query) has any problems on “antenna”(topic). a post: “Battery of iPhone is not good.” matches meronym pattern “PART of ENTITY”.

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

8. Intelligent Database Systems Lab Methodology- Credibility assessment module source credibility score of user I : Reduce interference of spammer content credibility score of user i in a time period TP : user's follower–followee ratio repost frequency threshold of followers/followees and number of the follwees set to be 10. the geometric mean of source credibility score : threshold of set to be 100. To prevent the improper abuse of credibility

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

10. Intelligent Database Systems Lab Methodology- Data collection, preprocessing, and data description 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): Data collection : preprocessing : 1.a copy of the opinion text was POS-tagged 2.the social networks of follower and followee relationships were constructed for further credibility analysis.

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

12. Intelligent Database Systems Lab Experiment - Topic detection effectiveness evaluation hashtag can’t split

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

14. Intelligent Database Systems Lab Experiment - Effects of Wikipedia Corpus Sizes

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

16. Intelligent Database Systems Lab Experiment - Score aggregation correctness evaluation >0.05

17. Intelligent Database Systems Lab Experiment - Topic-specific sentimental polarity MAE is only slightly improved

18. Intelligent Database Systems Lab 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. Intelligent Database Systems Lab Comments Advantages – effectively discover market intelligence (MI) for supporting decision-makers. Applications – Market trends.