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Ayako Hiramatsu Shingo Tamura Osaka Sangyo University Osaka University

Method for Atypical Opinion Extraction from Answers in Open-ended Questions (IEEE International Conference on Computational Cybernetics ICCC 2004). Ayako Hiramatsu Shingo Tamura Osaka Sangyo University Osaka University Hiroaki Oiso Norihisa Komoda Codetoys K. K. Osaka University. Abstract.

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Ayako Hiramatsu Shingo Tamura Osaka Sangyo University Osaka University

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  1. Method for Atypical Opinion Extraction from Answers in Open-ended Questions(IEEE International Conference on Computational Cybernetics ICCC 2004) Ayako Hiramatsu Shingo Tamura Osaka Sangyo University Osaka University Hiroaki Oiso Norihisa Komoda Codetoys K. K. Osaka University

  2. Abstract • Introduction • Open-ended questions vs. closed-ended questions • Atypical opinions vs. typical opinions • System • Aim • 3 Methods: ratio, distance, phrases • Experiment • Application experiment • Evaluation experiment • Conclusion

  3. Introduction (1/5) • Motivation: Mobile game market has been expanding rapidly  Game providers need to attract more users and prolong the subscription period per user  Subscribers answer questionnaires when canceling their accounts  Closed-ended and open-ended questions  Typical and atypical opinions

  4. Introduction (2/5) • Target game: mobile quiz game • In Japanese • Since 2002 • 3 carriers • Questions are answered by choosing a correct answer from 4 choices. • If consumers unsubscribe, all information is lost, and the questionnaire (closed-ended & open-ended questions) is given to be answered.

  5. Introduction (3/5) • Closed-ended questions: • Users are asked to choose from a limited number of pre-selected answers. • Unable to acquire unexpected ideas • Example:

  6. Introduction (4/5) • Open-ended questions: • Consumers can freely write opinions. • Not punctuated, ungrammatical, and abbreviated • Reveal dissatisfaction that cannot be captured in the closed-ended questions. • Few useful answers: most answers reflect opinions already known by closed-ended questions • Time-consuming to read all of the texts • Types: typical & atypical

  7. Introduction (5/5) • Typical & atypical open-ended questions:

  8. System (1/10) atypical • Aim: a system that efficiently extracts unexpectedly unique ideas by culling useless opinions from the data of open-ended question. • Outline: typical words: noun, adj, verb (ChaSen) “packet “ + “fee” “packet fee” 3 comparing method Next slide

  9. System (2/10) • Typical word database:

  10. System (3/10) • To extract atypical opinions • Compare the keywords of each opinion with the typical word database • 3 methods: • Based on the ratio of typical word combinations in the sentences • Consider the word order and the distance of difference between the positions of words • Divide the opinion into phrases at each typical word combination

  11. System (4/10) • Method 1: ratio • Remove opinions having neither keyword nor a noun keyword • Compare keywords with typical elements (the combination in the typical word database)

  12. System (5/10) • Example: • Formula 1: 2+2×1≧4 2+2×1≦6 typical α=2

  13. System (6/10) • Problems: • Misrecognition  method 2 • Long sentence  method 3 2+2×1≧4

  14. System (7/10) • Method 2: distance • Keyword distance d : the position difference of keywords • Modify typical elements: keyword distance is short, i.e. 2 keywords appearing near (d = 2) • Apply Formula 1

  15. System (8/10) • Example: 2+2×1≧4 0+2×0≦4

  16. System (9/10) • Method 3: phrases • Long sentences  few atypical elements  should NOT be omitted  sentences should be divided into phrases by delimiters • Delimiters: • Punctuation mark  pictograph (X) • Typical elements  (O) • Apply Formula 1 on phrases

  17. System (10/10) • Example:

  18. Application Experiment (1/2) • Compare the three proposed methods • Questionnaire data of users who unsubscribed from a certain carrier for 7 months • Content provider classified 3263 opinions = 2993 typical & 270 atypical opinions • About 8000 kinds of word combinations were registered to the typical word database

  19. Application Experiment (2/2) • Result: ratio distance phrases ANS 2993 270

  20. Evaluation Experiment (1/2) • Examine the best method: method 3 • Questionnaire data of users who unsubscribed from other carriers • Content providers classified 1764 opinions = 1589 typical & 175 atypical opinions • The typical word database is the same as in the application experiment.

  21. Evaluation Experiment (2/2) • Result: • The opinions with short sentences having 3 or 4 keywords  low recall  α=1  extract a huge number of atypical opinions  low precision  tradeoff LESS Satisfactory! ANS 1589 175

  22. Conclusion • Described a support system for atypical opinion extraction from answers in open-ended questions collected from consumers of mobile games when they unsubscribe • Proposed three methods of extraction of atypical opinions: ratio, distance, phrases • Differences of carriers also affect the accuracy of extraction.

  23. Q & A

  24. Delimiters insertion

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