微網誌使用者發文情緒對產品推薦效果之影響

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：14

、訪客IP：3.17.150.163

姓名

廖登豪(Teng-Hao Liao) 查詢紙本館藏

畢業系所

企業管理學系

論文名稱

微網誌使用者發文情緒對產品推薦效果之影響
(Microblog User Emotion and its Impact on the Recommendation Effectiveness)

相關論文

★ 在社群網站上作互動推薦及研究使用者行為對其效果之影響	★ 以AHP法探討伺服器品牌大廠的供應商遴選指標的權重決定分析
★ 以AHP法探討智慧型手機產業營運中心區位選擇考量關鍵因素之研究	★ 太陽能光電產業經營績效評估－應用資料包絡分析法
★ 建構國家太陽能電池產業競爭力比較模式之研究	★ 以序列採礦方法探討景氣指標與進出口值的關聯
★ ERP專案成員組合對績效影響之研究	★ 推薦期刊文章至適合學科類別之研究
★ 品牌故事分析與比較-以古早味美食產業為例	★ 以方法目的鏈比較Starbucks與Cama吸引消費者購買因素
★ 探討創意店家創業價值之研究- 以赤峰街、民生社區為例	★ 以領先指標預測企業長短期借款變化之研究
★ 應用層級分析法遴選電競筆記型電腦鍵盤供應商之關鍵因子探討	★ 以互惠及利他行為探討信任關係對知識分享之影響
★ 結合人格特質與海報主色以類神經網路推薦電影之研究	★ 資料視覺化圖表與議題之關聯

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 ( 永不開放)

摘要(中)

隨著社群網站的崛起，在虛擬世界互動交流的人數急速成長，其中的微網誌平臺更因使用者發文方便且容易吸引關注者回應的特性，所以張貼的訊息雖然簡短，累積的資料量卻十分龐大。這些訊息含有大量的情緒成分，卻不見學者研究如何利用這些當下表達的情緒，推薦可能投合訊息張貼者所需的產品或服務。
本研究採用卓淑玲、陳學志、鄭昭明（2013）的情緒字詞庫，採擷臺灣熱門微網誌Plurk使用者張貼訊息中的情緒成分，進而透過T檢定、Anova、相關分析及決策樹，探討不同的情緒成分對產品推薦效果的影響。
本研究的實驗結果發現，隱性的情緒誘發字詞比顯性的情緒描述字詞更能提升產品的推薦效果。在各種情緒狀態中，負向情緒、強烈情緒及單一情緒會帶來較佳的推薦效果。正向情緒字詞出現的頻率和產品推薦效果之間的正向關係，也同樣具有顯著效果。這些實驗結果，應有助於運用微網誌或類似平臺的產品或服務行銷商，研判如何集中有限的財務資源於最有可能實現銷售目標的潛在線上客戶。

摘要(英)

With the rise of social networking sites, the number of people participating in the virtual communities increases rapidly. Even though the messages they posted were brief, users of microblogs, a kind of social networking sites, have contributed an enormous amount of research data because of the characters of easy-posting and other unique features of the microblogging platforms. Although these messages contain a lot of emotional components, there was little, if any, work on how to use the emotion information on social media to recommend products or services to the users and their online circles.
In this study, we adopt emotion word database which provided by Cho et al. (2013) to extract emotional aspects from message posted to investigate the effectiveness of different emotional component on product recommendation.
The results of this study show that implicit emotion-inducing words are more effective than explicit emotion-describing words when recommending products. Besides, words with strong emotion are more effective than words with mild emotion and negative emotion are more effective than positive emotion and sentences with uniform emotion are more effective than mixed emotion. The research also finds that frequency of positive emotion words significant impact on recommendation effectiveness. The study should be helpful for product or service marketers who use Plurk or similar microbloging platforms to determine how to focus limited financial resources on potential online customers in order to achieve maximum sales revenue.

關鍵字(中)

★ 社群網站
★ 微網誌
★ 情緒字詞
★ 噗浪

關鍵字(英)

★ social network
★ microblog
★ emotion word
★ Plurk

論文目次

目錄 iv
表目錄 vi
圖目錄 vii
第一章緒論 1
1-1 研究背景與動機 1
1-2 研究目的 3
1-3 研究架構 3
第二章文獻探討 5
2.1 社群網站 5
2.2 情緒 7
2.3 情緒分析及應用 8
2.4 情緒字詞來源 10
第三章研究方法 12
3.1 Plurk社群網路 12
3.2 研究設計 13
3.3 系統推薦方法 13
3.4 研究假設 16
3.5 變數及分析方法說明 19
3.5.1 變數說明 19
3.5.2 分析方法 23
第四章研究實作 26
4.1 資料分析 26
4.1.1 資料前處理 26
4.1.2 實驗結果 26
4.1.2 假設結果討論 31
4.1.2.1 情緒負向字詞頻率不顯著原因 32
4.1.2.2 負向情緒信心水準比較低的可能理由 32
4.1.2.3 情緒描述詞於各情緒狀態下表現 35
4.2 決策樹 36
第五章結論與未來研究建議 40
5.1 研究結論 40
5.2 實務意涵 41
5.3 研究限制及未來研究建議 41
參考文獻 43
附錄一：218個情緒描詞在六個向度的分數（部分示例） 49
附錄二：395個情緒誘發詞在四個向度的分數（部分示例） 50

參考文獻

參考文獻
［1］李德治, 數學, 林孟儒, 數學, & 童惠玲. (2012). 統計學: 博碩文化.
［2］李德治, 數學, & 童惠玲. (2009). 多變量分析: 專題及論文常用的統計方法: 雙葉書廊.
［3］卓淑玲, 陳學志, & 鄭昭明. (2013). 台灣地區華人情緒與相關心理生理資料庫─ 中文情緒詞常模研究. Chinese Journal of Psychology, 55(4), 493-523.
［4］聯合新聞網. (2015). 為何百萬鐵粉愛噗浪勝於臉書…. Retrieved May.22, 2015, from
http://udn.com/news/story/7087/782456-%E7%82%BA%E4%BD%95%E7%99%BE%E8%90%AC%E9%90%B5%E7%B2%89-%E6%84%9B%E5%99%97%E6%B5%AA%E5%8B%9D%E6%96%BC%E8%87%89%E6%9B%B8%E2%80%A6
［5］ Alexa. (2014). Audience Geography. Where are this site′s(plurk.com) visitors located? , 2014, from http://www.alexa.com/siteinfo/plurk.com
［6］ Bagozzi, R. P., Gopinath, M., & Nyer, P. U. (1999). The role of emotions in marketing. Journal of the academy of marketing science, 27(2), 184-206.
［7］ Bai, S.-W. (2013). 考量時間因素的微網誌上產品推薦之研究.
［8］ Bakshy, E., Hofman, J. M., Mason, W. A., & Watts, D. J. (2011). Everyone′s an influencer: quantifying influence on twitter. Paper presented at the Proceedings of the fourth ACM international conference on Web search and data mining.
［9］ Balahur, A., Hermida, J. M., & Montoyo, A. (2012). Detecting implicit expressions of emotion in text: A comparative analysis. Decision Support Systems, 53(4), 742-753.
［10］ Balahur, A., & Steinberger, R. (2009). Rethinking opinion mining in newspaper articles: from theory to practice and back. Paper presented at the Proceedings of the first workshop on Opinion Mining and Sentiment Analysis (WOMSA 2009).
［11］ Banerjee, N., Chakraborty, D., Dasgupta, K., Mittal, S., Joshi, A., Nagar, S., . . . Madan, S. (2009). User interests in social media sites: an exploration with micro-blogs. Paper presented at the Proceedings of the 18th ACM conference on Information and knowledge management.
［12］ Brosch, T., Pourtois, G., & Sander, D. (2010). The perception and categorisation of emotional stimuli: A review. Cognition and emotion, 24(3), 377-400.
［13］ Chen, Y.-L., Cheng, L.-C., & Chuang, C.-N. (2008). A group recommendation system with consideration of interactions among group members. Expert systems with applications, 34(3), 2082-2090.
［14］ Chiu, C.-M., Hsu, M.-H., & Wang, E. T. (2006). Understanding knowledge sharing in virtual communities: An integration of social capital and social cognitive theories. Decision Support Systems, 42(3), 1872-1888.
［15］ Christensen, I. A., & Schiaffino, S. (2011). Entertainment recommender systems for group of users. Expert systems with applications, 38(11), 14127-14135.
［16］ Church, T., Katigbak, M. S., Reyes, J. A. S., & Jensen, S. M. (1998). Language and organisation of Filipino emotion concepts: comparing emotion concepts and dimensions across cultures. Cognition & emotion, 12(1), 63-92.
［17］ Cohen, J. B., Pham, M. T., & Andrade, E. B. (2008). The nature and role of affect in consumer behavior.
［18］ Colombetti, G. (2005). Appraising valence. Journal of consciousness studies, 12(8-10), 103-126.
［19］ Ekman, P. (1992). Are there basic emotions?
［20］ Esparza, S. G., O’Mahony, M. P., & Smyth, B. (2012). Mining the real-time web: A novel approach to product recommendation. Knowledge-Based Systems, 29, 3-11.
［21］ Fan, T.-K., & Chang, C.-H. (2010). Sentiment-oriented contextual advertising. Knowledge and information systems, 23(3), 321-344.
［22］ Fontaine, J. R., Scherer, K. R., Roesch, E. B., & Ellsworth, P. C. (2007). The world of emotions is not two-dimensional. Psychological science, 18(12), 1050-1057.
［23］ Frijda, N. H. (1993). Moods, emotion episodes, and emotions.
［24］ Galati, D., Sini, B., Tinti, C., & Testa, S. (2008). The lexicon of emotion in the neo-Latin languages. Social science information, 47(2), 205-220.
［25］ Gehm, T. L., & Scherer, K. R. (1988). Factors determining the dimensions of subjective emotional space.
［26］ Gilbert, E., & Karahalios, K. (2010). Widespread Worry and the Stock Market. Paper presented at the ICWSM.
［27］ Gillin, P. (2008). Secrets of Social Media Marketing: How to Use Online Conversations and Customer Communities to Turbo-charge Your Business! : Linden Publishing.
［28］ Glance, N., Hurst, M., & Tomokiyo, T. (2004). Blogpulse: Automated trend discovery for weblogs. Paper presented at the WWW 2004 workshop on the weblogging ecosystem: Aggregation, analysis and dynamics.
［29］ Grefenstette, G., Qu, Y., Shanahan, J. G., & Evans, D. A. (2004). Coupling Niche Browsers and Affect Analysis for an Opinion Mining Application. Paper presented at the RIAO.
［30］ Guo, J., Zhang, P., & Guo, L. (2012). Mining hot topics from Twitter streams. Procedia Computer Science, 9, 2008-2011.
［31］ Hill, S., Provost, F., & Volinsky, C. (2006). Network-based marketing: Identifying likely adopters via consumer networks. Statistical Science, 256-276.
［32］ Huffaker, D. (2010). Dimensions of leadership and social influence in online communities. Human Communication Research, 36(4), 593-617.
［33］ Jansen, B. J., Zhang, M., Sobel, K., & Chowdury, A. (2009). Micro-blogging as online word of mouth branding. Paper presented at the CHI′09 Extended Abstracts on Human Factors in Computing Systems.
［34］ Jiawei, H., & Kamber, M. (2001). Data mining: concepts and techniques. San Francisco, CA, itd: Morgan Kaufmann, 5.
［35］ Johnson-Laird, P. N., & Oatley, K. (1989). The language of emotions: An analysis of a semantic field. Cognition and emotion, 3(2), 81-123.
［36］ Joyce, E., & Kraut, R. E. (2006). Predicting continued participation in newsgroups. Journal of Computer‐Mediated Communication, 11(3), 723-747.
［37］ Kim, H., Suh, K.-S., & Lee, U.-K. (2013). Effects of collaborative online shopping on shopping experience through social and relational perspectives. Information & Management, 50(4), 169-180.
［38］ Kiss, C., & Bichler, M. (2008). Identification of influencers—measuring influence in customer networks. Decision Support Systems, 46(1), 233-253.
［39］ Kramer, A. D. (2010). An unobtrusive behavioral model of gross national happiness. Paper presented at the Proceedings of the SIGCHI Conference on Human Factors in Computing Systems.
［40］ Larsen, R. J., & Diener, E. (1992). Promises and problems with the circumplex model of emotion.
［41］ Lee, S. Y. M., Chen, Y., & Huang, C.-R. (2010). A text-driven rule-based system for emotion cause detection. Paper presented at the Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text.
［42］ Lerner, J. S., & Keltner, D. (2000). Beyond valence: Toward a model of emotion-specific influences on judgement and choice. Cognition & emotion, 14(4), 473-493.
［43］ Li, D., Lv, Q., Xie, X., Shang, L., Xia, H., Lu, T., & Gu, N. (2012). Interest-based real-time content recommendation in online social communities. Knowledge-Based Systems, 28, 1-12.
［44］ Li, F., & Du, T. C. (2011). Who is talking? An ontology-based opinion leader identification framework for word-of-mouth marketing in online social blogs. Decision Support Systems, 51(1), 190-197.
［45］ Li, Y.-M., & Lien, N.-J. (2009). An endorser discovering mechanism for social advertising. Paper presented at the Proceedings of the 11th International Conference on Electronic Commerce.
［46］ Li, Y.-M., Lin, C.-H., & Lai, C.-Y. (2010). Identifying influential reviewers for word-of-mouth marketing. Electronic Commerce Research and Applications, 9(4), 294-304.
［47］ Li, Y.-M., & Shiu, Y.-L. (2012). A diffusion mechanism for social advertising over microblogs. Decision Support Systems, 54(1), 9-22.
［48］ Lord, R. G., & Kanfer, R. (2002). Emotions and organizational behavior. Emotions in the workplace: Understanding the structure and role of emotions in organizational behavior, 5-19.
［49］ Mano, H. (1991). The structure and intensity of emotional experiences: Method and context convergence. Multivariate Behavioral Research, 26(3), 389-411.
［50］ McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a feather: Homophily in social networks. Annual review of sociology, 415-444.
［51］ Mishne, G. (2005). Experiments with mood classification in blog posts. Paper presented at the Proceedings of ACM SIGIR 2005 workshop on stylistic analysis of text for information access.
［52］ Mishne, G., & Glance, N. S. (2006). Predicting Movie Sales from Blogger Sentiment. Paper presented at the AAAI Spring Symposium: Computational Approaches to Analyzing Weblogs.
［53］ Ng, C. S.-P. (2013). Intention to purchase on social commerce websites across cultures: A cross-regional study. Information & Management, 50(8), 609-620.
［54］ Nguyen, T., Phung, D., Adams, B., & Venkatesh, S. (2013). Event extraction using behaviors of sentiment signals and burst structure in social media. Knowledge and information systems, 37(2), 279-304.
［55］ Niedenthal, P. M. (2008). Emotion concepts. Handbook of emotions, 587-600.
［56］ Ortony, A. (1990). The cognitive structure of emotions: Cambridge university press.
［57］ Pang, B., Lee, L., & Vaithyanathan, S. (2002). Thumbs up?: sentiment classification using machine learning techniques. Paper presented at the Proceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10.
［58］ Pennebaker, J. W., Mehl, M. R., & Niederhoffer, K. G. (2003). Psychological aspects of natural language use: Our words, our selves. Annual review of psychology, 54(1), 547-577.
［59］ Phelan, O., McCarthy, K., & Smyth, B. (2009). Using twitter to recommend real-time topical news. Paper presented at the Proceedings of the third ACM conference on Recommender systems.
［60］ Roseman, I. J. (1984). Cognitive determinants of emotion: A structural theory. Review of personality & social psychology.
［61］ Roseman, I. J., & Smith, C. A. (2001). Appraisal theory: Overview, assumptions, varieties, controversies.
［62］ Russell, J. A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology, 39(6), 1161.
［63］ Russell, J. A. (1983). Pancultural aspects of the human conceptual organization of emotions. Journal of Personality and Social Psychology, 45(6), 1281.
［64］ Russell, J. A., Lewicka, M., & Niit, T. (1989). A cross-cultural study of a circumplex model of affect. Journal of Personality and Social Psychology, 57(5), 848.
［65］ Russell, J. A., & Mehrabian, A. (1977). Evidence for a three-factor theory of emotions. Journal of research in Personality, 11(3), 273-294.
［66］ Scherer, K., & Wallbott, H. (1997). The ISEAR questionnaire and codebook. Geneva Emotion Research Group.
［67］ Scherer, K. R. (1987). Toward a dynamic theory of emotion. Geneva studies in Emotion, 1, 1-96.
［68］ Schumaker, R. P., Zhang, Y., Huang, C.-N., & Chen, H. (2012). Evaluating sentiment in financial news articles. Decision Support Systems, 53(3), 458-464.
［69］ Shah, K., Munshi, N., & Reddy, P. (2013). Sentiment Analysis and Opinion Mining of Microblogs. University of Illinois at Chicago, Course CS.
［70］ Shaver, P. R., Murdaya, U., & Fraley, R. C. (2001). Structure of the Indonesian emotion lexicon. Asian journal of social psychology, 4(3), 201-224.
［71］ Smith, C. A., & Ellsworth, P. C. (1985). Patterns of cognitive appraisal in emotion. Journal of Personality and Social Psychology, 48(4), 813.
［72］ Smith, S. M., & Petty, R. E. (1996). Message framing and persuasion: A message processing analysis. Personality and Social Psychology Bulletin, 22, 257-268.
［73］ Sprenger, T. O., Tumasjan, A., Sandner, P. G., & Welpe, I. M. (2014). Tweets and trades: The information content of stock microblogs. European Financial Management, 20(5), 926-957.
［74］ Subasic, P., & Huettner, A. (2001). Affect analysis of text using fuzzy semantic typing. Fuzzy Systems, IEEE Transactions on, 9(4), 483-496.
［75］ Susskind, A. M. (2002). I told you so! Restaurant customers’ word-of-mouth communication patterns.
［76］ Talmy, L. (2001). Toward a cognitive semantics. Vol. 1: Concept-structuring systems. Vol. 2: Typology and process in concept structuring: Cambridge, Ma: MIT Press.
［77］ Thayer, R. E. (2003). Calm energy: How people regulate mood with food and exercise: Oxford University Press.
［78］ Thelwall, M., Buckley, K., & Paltoglou, G. (2011). Sentiment in Twitter events. Journal of the American Society for Information Science and Technology, 62(2), 406-418.
［79］ Tumasjan, A., Sprenger, T. O., Sandner, P. G., & Welpe, I. M. (2010). Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment. ICWSM, 10, 178-185.
［80］ Turney, P. D. (2002). Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews. Paper presented at the Proceedings of the 40th annual meeting on association for computational linguistics.
［81］ Van den Bulte, C., & Wuyts, S. (2007). Social Networks and Marketing (Marketing Science Institute, Cambridge, MA).
［82］ Wang, K.-Y., Ting, I.-H., & Wu, H.-J. (2013). Discovering interest groups for marketing in virtual communities: An integrated approach. Journal of Business Research, 66(9), 1360-1366.
［83］ Watson, D., & Tellegen, A. (1985). Toward a consensual structure of mood. Psychological bulletin, 98(2), 219.
［84］ Xu, K. (2011). Mining and analyzing customer opinions/sentiments of Web 2.0 for business applications.
［85］ Yoshida, M., Kinase, R., Kurokawa, J., & Yashiro, S. (1970). Multi-dimensional scaling of emotion. Japanese psychological research, 12(2), 45-61.
［86］ Zhang, Z., & Li, X. (2010). Controversy is Marketing: Mining Sentiments in Social Media. Paper presented at the hicss.

指導教授

許秉瑜(Ping-Yu Hsu)

審核日期

2016-9-22

推文