當資訊需求者欲掌握其感興趣產品/服務之評價時，其往往透過各網路論壇瀏覽與該產品/服務相關之評論。而資訊需求者往往依其個人偏好挑選評論進行閱讀，忽略其餘評論內容。此外，資訊需求者往往將各評論內容中提及之產品/服務之各關注項目的評價水準以同等代表性程度進行評價彙整，而忽略各評論內容之代表性程度不同、其對產品/服務之各關注項目評價彙整的影響程度實不相同，導致資訊需求者無法適切地彙整各則具不同代表性程度的評論所提及之產品/服務之各關注項目的評價水準。為解決此問題，本研究首先解析自各網路論壇蒐集之產品/服務相關評論，並釐清各評論內容所包含之特徵屬性（包括評論發佈日期、留言者等級、留言者涉入程度等）。接著，依前述解析結果，本研究乃發展一套「評論關注點、評論傾向與內容代表性之綜合評估模式」方法論，此方法可擷取各評論內容中之特徵屬性，並依五項代表性相關特徵屬性分析一評論之評價代表性。之後，此方法乃將所有評論內容探討之產品/服務關注項目評價水準依不同評論之代表性程度進行評價彙整，以產生一產品/服務關注項目之評價結論，並以視覺化方式將評價趨勢及評價結論呈現予資訊需求者，以利資訊需求者準確作出最終消費決策。

Once a customer wants to capture critical information from the comments for a product/service, he/she has to search for the comments related to the product/service via the Internet. As the customer browses the comments, he/she usually has to spend much time to acquire the emotional tendency of the product/service. Furthermore, the customer often regards that the value of each comment is of equal importance. That is, the representativeness of distinct comments is usually ignored. In order to solve this problem, this research develops a model for comment value evaluation. The proposed model can be used to visually reveal the values of distinct comments and the integrated evaluation of the product/service. By utilizing this model, the customer can easily acquire representativeness of each comment and the integrated evaluation of product/service characteristics in order to efficiently make purchase decisions.

1. Abrahams, A. S., Jiao, J., Wang, G. A. and Fan, W., 2012, “Vehicle Defect Discovery from Social Media,” Decision Support Systems, Vol. 54, pp. 87-97.
2. Aramaki, E., Maskawa, S. and Morita, M., 2011, “Twitter Catches the Flu: Detecting Influenza Epidemics Using Twitter,” Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 1568-1576.
3. Azmandian, M., Singh, K., Gelsey, B., Chang, Y. H. and Maheswaran, R., 2012, “Following Human Mobility Using Tweets,” International Workshop on Agents and Data Mining Interaction, pp. 139-149.
4. Barnett, G. A., Xu, W. W., Chu, J., Jiang, K., Huh, C., Park, J. Y. and Park, H. W., 2016, “Measuring International Relations in Social Media Conversations,” Government Information Quarterly, pp. 1-8.
5. Bello, G., Menendez, H., Okazaki, S. and Camacho, D., 2013, “Extracting Collective Trends from Twitter Using Social-Based Data Mining,” International Conference on Computational Collective Intelligence, pp. 622-630.
6. Benevenuto, F., Magno, G., Rodrigues, T. and Almeida, V., 2010, “Detecting Spammers on Twitter,” Proceedings of the Collaboration, Electronic Messaging, Anti-Abuse and Spam Conference, Vol. 6, pp. 12-21.
7. Bergsma, S., Dredze, M., Van Durme, B., Wilson, T. and Yarowsky, D., 2013, “Broadly Improving User Classification via Communication-Based Name and Location Clustering on Twitter,” Proceedings of the 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics in Human Language Technologies, pp. 1010-1019.
8. Bjorkelund, E., Burnett, T. H. and Norvag, K., 2012, “A Study of Opinion Mining and Visualization of Hotel Reviews,” Proceedings of the 14th International Conference on Information Integration and Web-based Applications and Services, pp. 229-238.
9. Bonzanini, M., Martinez-Alvarez, M. and Roelleke, T., 2012, “Opinion Summarization through Sentence Extraction: An Investigation with Movie Reviews,” Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1121-1122.
10. Chen, Y. H., Lu, J. L. and Tsai, M. F., 2014, “Finding Keywords in Blogs: Efficient Extraction in Blog Mining via User Bahaviors,” Expert Systems with Applications, Vol. 41, pp. 663-670.
11. Chu, Z., Gianvecchio, S., Wang, H. and Jajodia, S., 2012, “Detecting Automation of Twitter Accounts: Are You a Human, Bot, or Cyborg,” IEEE Transactions on Dependable and Secure Computing, Vol. 9, pp. 811-824.
12. Coppersmith, G., Leary, R., Whyne, E. and Wood, T., 2015, “Quantifying Suicidal Ideation via Language Usage on Social Media,” Proceedings of the Statistics Meetings in Statistical Computing Section, pp. 1-16.
13. Culotta, A., 2010, “Towards Detecting Influenza Epidemics by Analyzing Twitter Messages,” Proceedings of the First Workshop on Social Media Analytics, pp. 115-122.
14. Diakopoulos, N., Naaman, M. and Kivran-Swaine, F., 2010, “Diamonds in the Rough: Social Media Visual Analytics for Journalistic Inquiry,” Visual Analytics Science and Technology in IEEE Symposium, pp. 115-122.
15. Endarnoto, S. K., Pradipta, S., Nugroho, A. S. and Purnama, J., 2011, “Traffic Condition Information Extraction and Visualization from Social Media Twitter for Andoris Mobile Application,” International Conference on Electrical Engineering and Informatics, pp. 1-4.
16. Feldman, R., Fresco, M., Goldenberg, J., Netzer, O. and Ungar, L., 2007, “Extracting Product Comparisons from Discussion Boards,” Proceeding of the 7th IEEE International Conference on Data Mining, pp. 469-474.
17. Feng, S., Wang, D., Yu, G., Yang, C. and Yang, N., 2009, “Chinese Blog Clustering by Hidden Sentiment Factors,” Advanced Data Mining and Applications, pp. 140-151.
18. Gao, J. and Lai, W., 2010, “Formal Concept Analysis Based Clustering for Blog Network Visualization,” Proceedings of the International Conference on Advanced Data Mining and Applications, pp. 394-404.
19. Godin, F., Slavkovikj, V., De Neve, W., Schrauwen, B. and Van de Walle, R., 2013, “Using Topic Models for Twitter Hashtag Recommendation,” Proceedings of the 22nd International Conference on World Wide Web, pp. 593-596.
20. Gregory, M. L., Chinchor, N., Whitney, P., Carter, R., Hetzler, E. and Turner, A., 2006, “User-Directed Sentiment Analysis: Visualizing the Affective Content of Documents,” Proceedings of the Workshop on Sentiment and Subjectivity in Text, pp. 23-30.
21. Ifrim, G., Shi, B. and Brigadir, I., 2014, “Event Detection in Twitter Using Aggressive Filtering and Hierarchical Tweet Clustering,” Association for Computing Machinery, pp. 1-7.
22. Ikeda, D., Takamura, H. and Okumura, M., 2008, “Semi-Supervised Learning for Blog Classification,” Proceedings of the Association for the Advancement of Artificial Intelligence, pp. 1156-1161.
23. Ji, X., Chun, S. A. and Geller, J., 2012, “Epidemic Outbreak and Spread Detection System Based on Twitter Data,” International Conference on Health Information Science, pp. 152-163.
24. Ji, X., Chun, S. A. and Geller, J., 2013, “Monitoring Public Health Concerns Using Twitter Sentiment Classifications,” IEEE International Conference on Healthcare Informatics, pp. 335-344.
25. Koga, H. and Taniguchi, T., 2011, “Developing a User Recommendation Engine on Twitter Using Estimated Latent Topics,” International Conference on Human-Computer Interaction, pp. 461-470.
26. Kongthon, A., Haruechaiyasak, C., Pailai, J. and Kongyoung, S., 2012, “The Role of Twitter During a Natural Disaster: Case Study of 2011 Thai Flood,” Technology Management for Emerging Technologies, pp. 2227-2232.
27. Kurashima, T., Tezuka, T. and Tanaka, K., 2005, “Blog Map of Experiences: Extracting and Geographically Mapping Visitor Experiences from Urban Blogs,” International Conference on Web Information Systems Engineering, pp. 496-503.
28. Liu, X., Wang, Y., Li, Y. and Shi, B., 2011, “Identifying Topic Experts and Topic Communities in the Blogspace,” Proceeding of the International Conference on Database Systems for Advanced Applications, pp. 68-77.
29. Liu, Z., Chen, X. and Sun, M., 2012, “Mining the Interests of Chinese Microbloggers via Keyword Extraction,” Frontiers of Computer Science, Vol. 6, pp. 76-87.
30. Marcus, A., Bernstein, M. S., Badar, O., Karger, D. R., Madden, S. and Miller, R. C., 2011, “Twitinfo: Aggregating and Visualizing Microblogs for Event Exploration,” Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 227-236.
31. Meyer, B., Bryan, K., Santos, Y. and Kim, B., 2011, “TwitterReporter: Breaking News Detection and Visualization through the Geo-Tagged Twitter Network,” Computer Science Faculty Presentations, pp. 84-89.
32. Miller, Z., Dickinson, B., Deitrick, W., Hu, W. and Wang, A. H., 2014, “Twitter Spammer Detection Using Data Stream Clustering,” Information Sciences, Vol. 260, pp. 64-73.
33. Mohamed, M. M., 2013, “More than Words: Social Networks’ Text Mining for Consumer Brand Sentiments,” Expert Systems with Applications, Vol. 40, pp. 4241-4251.
34. Nakaji, Y. and Yanai, K., 2012, “Visualization of Real-World Events with Geotagged Tweet Photos,” IEEE International Conference on Multimedia and Expo Workshops, pp. 272-277.
35. Negi, S. and Buitelaar, P., 2015, “Towards the Extraction of Customer-to-Customer Suggestions from Reviews,” Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 2159-2167.
36. Ni, X., Xue, G. R., Ling, X., Yu, Y. and Yang, Q., 2007, “Exploring in the Weblog Space by Detecting Informative and Affective Articles,” Proceedings of the 16th International Conference on World Wide Web, pp. 281-290.
37. Nivedha, R. and Sairam, N., 2015, “A Machine Learning Based Classification for Social Media Messages,” Indian Journal of Science and Technology, Vol. 8, pp.1-4.
38. Pan, S. J., Ni, X., Sun, J. T., Yang, Q. and Chen, Z., 2010, “Cross-Domain Sentiment Classification via Spectral Feature Alignment,” Proceedings of the 19th International Conference on World Wide Web, pp. 751-760.
39. Paul, M. J. and Dredze, M., 2011, “You Are What You Tweet: Analyzing Twitter for Public Health,” Proceeding of the International Conference on Weblogs and Social Media, Vol. 20, pp. 265-272.
40. Petkos, G., Papadopoulos, S. and Kompatsiaris, Y., 2014, “Two-Level Message Clustering for Topic Detection in Twitter,” Proceeding of the SNOW Workshop, pp. 49-56.
41. Phuvipadawat, S. and Murata, T., 2010, “Breaking News Detection and Tracking in Twitter,” Proceeding of the International Conference on Web Intelligence and Intelligent Agent Technology, Vol. 3, pp. 120-123.
42. Preethi, P. G., Uma, V. and Kumar, A., 2015, “Temporal Sentiment Analysis and Causal Rules Extraction from Tweets for Event Prediction,” Procedia Computer Science, Vol. 48, pp. 84-89.
43. Rakesh, V., Reddy, C. K. and Singh, D., 2013, “Location-Specific Tweet Detection and Topic Summarization in Twitter,” Proceedings of the ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 1441-1444.
44. Rosa, K. D., Shah, R., Lin, B., Gershman, A. and Frederking, R., 2011, “Topical Clustering of Tweets,” Proceedings of the ACM Conference, pp. 1-8.
45. Sakaki, T., Okazaki, M. and Matsuo, Y., 2010, “Earthquake Shakes Twitter Users: Real-Time Event Detection by Social Sensors,” Proceedings of the 19th International Conference on World Wide Web, pp. 851-860.
46. Singh, V. K., Waila, P., Piryani, R. and Uddin, A., 2013, “Computational Exploration of Theme-Based Blog Data Using Topic Modeling NERC and Sentiment Classifier Combine,” AASRI Conference on Intelligent Systems and Control, pp. 212-222.
47. Thom, D., Bosch, H., Koch, S., Worner, M. and Ertl, T., 2012, “Spatiotemporal Anomaly Detection through Visual Analysis of Geolocated Twitter Messages,” Visualization Symposium, pp. 41-48.
48. Tomokiyo, T. and Hurst, M., 2003, “A Language Model Approach to Keyphrase Extraction,” Proceedings of the ACL Workshop on Multiword Expressions: Analysis, Acquisition and Treatment, Vol. 18, pp. 33-40.
49. Tsagkalidou, K., Koutsonikola, V., Vakali, A. and Kafetsios, K., 2011, “Emotional Aware Clustering on Micro-Blogging Sources,” Computing and Intelligent Interaction, pp. 387-396.
50. Tumasjan, A., Sprenger, T. O., Sandner, P. G. and Welpe, I. M., 2010. “Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment,” Proceeding of the International Conference on Weblogs and Social Media, Vol. 10, pp. 178-185.
51. Varga, I., Sano, M., Torisawa, K., Hashimoto, C., Ohtake, K., Kawai, T., Jong-Hoon O. and De Saeger, S., 2013, “Aid is out There: Looking for Help from Tweets during a Large Scale Disaster,” Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, pp. 1619-1629.
52. Wanner, F., Rohrdantz, C., Mansmann, F., Oelke, D. and Keim, D. A., 2009, “Visual Sentiment Analysis of RSS News Feeds Featuring the US Presidential Election in 2008,” Workshop on Visual Interfaces to the Social and the Semantic Web, pp. 1-8.
53. Wu, W., Zhang, B. and Ostendorf, M., 2010, “Automatic Generation of Personalized Annotation Tags for Twitter Users,” Proceedings of the Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 689-692.
54. Yu, B. and Kwok, L., 2011, “Classifying Business Marketing Messages on Facebook,” Association for Computing Machinery Special Interest Group on Information Retrieval, pp. 1-5.
55. Zhao, S., Zhong, L., Wickramasuriya, J. and Vasudevan, V., 2011, “Human as Real-Time Sensors of Social and Physical Events: A Case Study of Twitter and Sports Games,” ArXiv Preprint, pp. 1106-1115.