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Effects of sentiment on recommendations in social network

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Abstract

This study adopted a sentiment word database to extract sentiment-related data from microblog posts. These data were then used to investigate the effect of different types of sentiment-related words on product recommendations. The results indicate that posts containing strong sentiments received more clicks than posts containing neutral sentiments. Posts containing more than one positive sentiment word generate more effective recommendations than posts containing only one positive sentiment word. This study also demonstrated that posts with a negative polarity classification received more clicks than those with a positive polarity classification. Additionally, the microblog posts containing implicit sentiment words received more clicks than those containing explicit sentiment words. The findings presented here could assist product or service marketers who use Plurk or similar microblogging platforms better focus their limited financial resources on potential online customers to achieve maximum sale revenue.

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References

  • Alahmadi, D. H., & Zeng, X. J. (2015). ISTS: Implicit social trust and sentiment based approach to recommender systems. Expert Systems with Applications, 42(22), 8840–8849.

    Article  Google Scholar 

  • Balahur, A., & Steinberger, R. (2009). Rethinking opinion mining in newspaper articles: From theory to practice and back. In Proceedings of the first workshop on opinion mining and sentiment analysis (pp. 1–12). Seville, Spain: University of Sevilla.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Banerjee, N., Chakraborty, D., Dasgupta, K., Mittal, S., Joshi, A., Nagar, S., et al. (2009). User interests in social media sites: An exploration with micro-blogs. In Proceedings of the 18th ACM conference on information and knowledge management (pp. 1823–1826). Hong Kong: ACM.

    Google Scholar 

  • Bao, S., Nitta, T., Shindou, D., Yanagisawa, M., & Togawa, N. (2015). A landmark-based route recommendation method for pedestrian walking strategies. In IEEE 4th global conference on consumer electronics (pp. 672–673). Osaka, Japan: IEEE.

    Google Scholar 

  • Bolte, A., Goschke, T., & Kuhl, J. (2003). Emotion and intuition: Effects of positive and negative mood on implicit judgments of semantic coherence. Psychological Science, 14(5), 416–421.

    Article  Google Scholar 

  • Bruce, R., & Wiebe, J. (1994). Word-sense disambiguation using decomposable models. In Proceedings of the 32nd annual meeting on association for computational linguistics (pp. 139–146). Las Cruces: Association for Computational Linguistics.

    Chapter  Google Scholar 

  • Chandra, Y., Jiang, L. C., & Wang, C. J. (2016). Mining social entrepreneurship strategies using topic modeling. PLoS One, 11(3), e0151342.

    Article  Google Scholar 

  • Cho, S. L., Chen, H. C., & Cheng, C. M. (2013). Taiwan corpora of Chinese sentiments and relevant psychophysiological data-a study on the norm of Chinese sentimental words. Chinese Journal of Psychology, 55(4), 493–523.

    Google Scholar 

  • Colombetti, G. (2005). Appraising valence. Journal of Consciousness Studies, 12(8–10), 103–126.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Fariss, C. J., Linder, F. J., Jones, Z. M., Crabtree, C. D., Biek, M. A., Ross, A. S. M., Kaur, T., & Tsai, M. (2015). Human rights texts: Converting human rights primary source documents into data. PLoS One, 10(9), e0138935.

    Article  Google Scholar 

  • Fontaine, J. R., Scherer, K. R., Roesch, E. B., & Ellsworth, P. C. (2007). The world of sentiments is not two-dimensional. Psychological Science, 18(12), 1050–1057.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Gavalas, D., Kenteris, M., Konstantopoulos, C., & Pantziou, G. (2012). Web application for recommending personalised mobile tourist routes. IET Software, 6(4), 313–322.

    Article  Google Scholar 

  • Gavalas, D., Kasapakis, V., Konstantopoulos, C., Pantziou, G., Vathis, N., & Zaroliagis, C. (2015). The eCOMPASS multimodal tourist tour planner. Expert Systems with Applications, 42(21), 7303–7316.

    Article  Google Scholar 

  • Grimmer, J. (2010). A bayesian hierarchical topic model for political texts: Measuring expressed agendas in senate press releases. Political Analysis, 18(1), 1–35.

    Article  Google Scholar 

  • Guo, G., Zhang, J., & Yorke-Smith, N. (2016). A novel recommendation model regularized with user trust and item ratings. IEEE Transactions on Knowledge and Data Engineering, 28(7), 1607–1620.

    Article  Google Scholar 

  • Guy, I., Zwerdling, N., Ronen, I., Carmel, D., & Uziel, E. (2010). Social media recommendation based on people and tags. In Proceedings of the 33rd international ACM SIGIR conference on research and development in information retrieval (pp. 194–201). Haifa: ACM.

    Google Scholar 

  • Hatzivassiloglou, V., & McKeown, K. R. (1997). Predicting the semantic orientation of adjectives. In Proceedings of the eighth conference on European chapter of the association for computational linguistics (pp. 174–181). Spain: Association for Computational Linguistics.

    Chapter  Google Scholar 

  • Hidasi, B., & Tikk, D. (2016). General factorization framework for context-aware recommendations. Data Mining and Knowledge Discovery, 30(2), 342–371.

    Article  Google Scholar 

  • Hogenboom, A., van Iterson, P., Heerschop, B., Frasincar, F., & Kaymak, U. (2011). Determining negation scope and strength in sentiment analysis. In IEEE international conference on systems, man, and cybernetics (pp. 2589–2594). The Netherlands: IEEE.

  • Huffaker, D. (2010). Dimensions of leadership and social influence in online communities. Human Communication Research, 36(4), 593–617.

    Article  Google Scholar 

  • Jang, H. J., Sim, J., Lee, Y., & Kwon, O. (2013). Deep sentiment analysis: Mining the causality between personality-value-attitude for analyzing business ads in social media. Expert Systems with Applications, 40(18), 7492–7503.

    Article  Google Scholar 

  • Johnson-Laird, P. N., & Oatley, K. (1989). The language of emotions: An analysis of a semantic field. Cognition & Emotion, 3(2), 81–123.

    Article  Google Scholar 

  • Joyce, E., & Kraut, R. E. (2006). Predicting continued participation in newsgroups. Journal of Computer-Mediated Communication, 11(3), 723–747.

    Article  Google Scholar 

  • Kevin, M., Tom, L., Kevin, C., & Aiden, M. (2016). Aggregating social media data with temporal and environmental context for recommendation in a mobile tour guide system. Journal of Hospitality and Tourism Technology, 7(3), 281–299.

    Article  Google Scholar 

  • Koole, S. L., & Rothermund, K. (2011). I feel better but I don't know why: The psychology of implicit emotion regulation. Cognition & Emotion, 25(3), 389–399.

    Article  Google Scholar 

  • Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis lectures on human language technologies, 5(1), 1–167.

    Article  Google Scholar 

  • Liu, Y. C., & Lin, C. W. (2012). A new method to compose long unknown Chinese keywords. Journal of Information Science, 38(4), 366–382.

    Article  Google Scholar 

  • Majid, A., Chen, L., Chen, G., Mirza, H. T., Hussain, I., & Woodward, J. (2013). A context-aware personalized travel recommendation system based on geotagged social media data mining. International Journal of Geographical Information Science, 27(4), 662–684.

    Article  Google Scholar 

  • Mano, H. (1991). The structure and intensity of sentimental experiences: Method and context convergence. Multivariate Behavioral Research, 26(3), 389–411.

    Article  Google Scholar 

  • Neri, F., Aliprandi, C., Capeci, F., Cuadros, M., & By, T. (2012). Sentiment analysis on social media. In Proceedings of the 2012 international conference on advances in social networks analysis and mining (pp. 951–958). Washington: IEEE Computer Society.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Onan, A., Korukoğlu, S., & Bulut, H. (2016). Ensemble of keyword extraction methods and classifiers in text classification. Expert Systems with Applications, 57, 232–247.

    Article  Google Scholar 

  • Ong, S. Y. (2016). The impact of user sentiment aroused by the-day-of-the-week on the recommendation effectiveness in microblog. TaoYu, Z., Wang, Z., Chen, L., Guo, B., & Li, W. (2016)an City, Taiwan: National Central University.

  • Ortony, A., Clore, G. L., & Collins, A. (1990). The cognitive structure of emotions. Australia: Cambridge University Press.

    Google Scholar 

  • Pang, B., Lee, L., & Vaithyanathan, S. (2002). Thumbs up? Sentiment classification using machine learning techniques. In Proceedings of the ACL-02 conference on empirical methods in natural language processing (pp. 79–86). Stroudsburg, PA, USA: Association for Computational Linguistics.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Phelan, O., McCarthy, K., & Smyth, B. (2009). Using twitter to recommend real-time topical news. In Proceedings of the third ACM conference on recommender systems (pp. 385–388). New York: ACM.

    Google Scholar 

  • Quinn, K. M., Monroe, B. L., Colaresi, M., Crespin, M. H., & Radev, D. R. (2010). How to analyze political attention with minimal assumptions and costs. American Journal of Political Science, 54(1), 209–228.

    Article  Google Scholar 

  • Russell, J. A. (1983). Pancultural aspects of the human conceptual organization of sentiments. Journal of Personality and Social Psychology, 45(6), 1281–1288.

    Article  Google Scholar 

  • Russell, J. A., & Mehrabian, A. (1977). Evidence for a three-factor theory of emotions. Journal of Research in Personality, 11(3), 273–294.

    Article  Google Scholar 

  • Santu, S. K. K., Sondhi, P., & Zhai, C. (2016). Generative feature language models for mining implicit features from customer reviews. In Proceedings of the 25th ACM international on conference on information and knowledge management (pp. 929–938). New York: ACM.

    Google Scholar 

  • Scherer, K. R. (1999). Appraisal Theory. In Appraisal theory. New York: John Wiley & Sons.

    Chapter  Google Scholar 

  • Schumaker, R. P., Zhang, Y., Huang, C. N., & Chen, H. (2012). Evaluating sentiment in financial news articles. Decision Support Systems, 53(3), 458–464.

    Article  Google Scholar 

  • Singh, R., & Kaur, R. (2015). Sentiment analysis on social media and online review. International Journal of Computer Applications, 121(20), 44–48.

    Article  Google Scholar 

  • Single Grain. (2017). https://www.singlegrain.com/social-media-news/facebook-news-feed-algorothm/ Accessed 30 August 2017.

  • SlideShare. (2017). https://www.slideshare.net/bexdeep/plurk-analysis-4136802/ Accessed 30 August 2017.

  • Smith, C. A., & Ellsworth, P. C. (1985). Patterns of cognitive appraisal in sentiment. Journal of Personality and Social Psychology, 48(4), 813–838.

    Article  Google Scholar 

  • Smith, S. M., & Petty, R. E. (1996). Message framing and persuasion: A message processing analysis. Personality and Social Psychology Bulletin, 22(3), 257–268.

    Article  Google Scholar 

  • Song, H., Chu, J., Hu, Y., & Liu, X. (2013). Semantic analysis and implicit target extraction of comments from e-commerce websites. In 2013 fourth world congress on software engineering (pp. 331–335). Piscataway, NJ: IEEE.

    Chapter  Google Scholar 

  • Stieglitz, S., & Dang-Xuan, L. (2013). Emotions and information diffusion in social media—Sentiment of microblogs and sharing behavior. Journal of Management Information Systems, 29(4), 217–248.

    Article  Google Scholar 

  • Talmy, L. (2001). Toward a cognitive semantics. Vol. 1: Concept-structuring systems. Vol. 2: Typology and process in concept structuring. Cambridge: MIT Press.

  • Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., & Kappas, A. (2010). Sentiment strength detection in short informal text. Journal of the American Society for Information Science and Technology, 61(12), 2544–2558.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Thelwall, M., Buckley, K., & Paltoglou, G. (2012). Sentiment strength detection for the social web. Journal of the American Society for Information Science and Technology, 63(1), 163–173.

    Article  Google Scholar 

  • Turney, P. D. (2002). Thumbs up or thumbs down?: Semantic orientation applied to unsupervised classification of reviews. In Proceedings of the 40th annual meeting on association for computational linguistics (pp. 417–424). Philadelphia: Association for Computational Linguistics.

    Google Scholar 

  • Valcarce, D., Parapar, J., & Barreiro, Á. (2016). Item-based relevance modelling of recommendations for getting rid of long tail products. Knowledge-Based Systems, 103, 41–51.

    Article  Google Scholar 

  • Wang, K. Y., Ting, I. H., Wu, S., & Liu, Q. (2013). Discovering interest groups for marketing in virtual communities: An integrated approach. Journal of Business Research, 66(9), 1360–1366.

    Article  Google Scholar 

  • Watson, D., & Tellegen, A. (1985). Toward a consensual structure of mood. Psychological Bulletin, 98(2), 219–235.

    Article  Google Scholar 

  • Wei, T. H. (2016). The effect of event sentiment on product recommendation in a microblog platform. TaoYu, Z., Wang, Z., Chen, L., Guo, B., & Li, W. (2016) an City, Taiwan: National Central University.

  • Wilson, T., Wiebe, J., & Hoffmann, P. (2005). Recognizing contextual polarity in phrase-level sentiment analysis. In Proceedings of the conference on human language technology and empirical methods in natural language processing. Stroudsburg, PA: Association for Computational Linguistics.

    Google Scholar 

  • Wu, S., Liu, Q., Wang, L., Tan, T., Liu, S. Q., Wang, L., & Tan, T. (2016). Contextual operation for recommender systems. IEEE Transactions on Knowledge and Data Engineering, 28(8), 2000–2012.

    Article  Google Scholar 

  • Xu, K. (2011). Mining and analyzing customer opinions/sentiments of web 2.0 for business applications. Hong Kong: City University of Hong Kong.

    Google Scholar 

  • Xu, Y., Hu, T., & Li, Y. (2016). A travel route recommendation algorithm with personal preference. In 2016 12th international conference on natural computation, fuzzy systems and knowledge discovery (pp. 390–396). Piscataway, NJ: IEEE.

    Google Scholar 

  • Xue, S., & Liu, S. (2015). Algorithm research of individualized travelling route recommendation based on similarity. In MATEC Web of Conferences. Les Ulis, France: EDP Sciences.

    Google Scholar 

  • Yang, G. (2011). Technology and its contents: Issues in the study of the Chinese internet. The Journal of Asian Studies, 70(4), 1043–1050.

    Article  Google Scholar 

  • Yessenov, K., & Misailovi, S. (2009) Sentiment analysis of movie review comments, 6.863 spring 2009 final project. Kuat Yessenov kuat@csail.mit.edu. Saša Misailovic.

  • Yigit, M., Bilgin, B. E., & Karahoca, A. (2015). Extended topology based recommendation system for unidirectional social networks. Expert Systems with Applications, 42(7), 3653–3661.

    Article  Google Scholar 

  • Yu, Z., Wang, Z., Chen, L., Guo, B., & Li, W. (2016). Featuring, detecting, and visualizing human sentiment in Chinese micro-blog. ACM Transactions on Knowledge Discovery from Data, 10(4), 1–23.

    Article  Google Scholar 

  • Zhang, Z. (2008). Weighing stars: Aggregating online product reviews for intelligent e-commerce applications. IEEE Intelligent Systems, 23(5), 42–49.

    Article  Google Scholar 

  • Zheng, X., Zhu, S., & Lin, Z. (2013). Capturing the essence of word-of-mouth for social commerce: Assessing the quality of online e-commerce reviews by a semi-supervised approach. Decision Support Systems, 56, 211–222.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Business Administration, National Central University, No.300, Jhongda Rd., Jhongli Dist, Taoyuan City, 32001, Taiwan, Republic of China

    Ping-Yu Hsu, Hong-Tsuen Lei, Shih-Hsiang Huang, Teng Hao Liao & Yao-Chung Lo

  2. Taroko Software, 11F. No.311, Sec. 3, Nanjing E. Rd., Songshan Dist, Taipei City, 105, Taiwan, Republic of China

    Chin-Chun Lo

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  1. Ping-Yu Hsu
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  2. Hong-Tsuen Lei
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  3. Shih-Hsiang Huang
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  4. Teng Hao Liao
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Correspondence to Ping-Yu Hsu.

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Hsu, PY., Lei, HT., Huang, SH. et al. Effects of sentiment on recommendations in social network. Electron Markets 29, 253–262 (2019). https://doi.org/10.1007/s12525-018-0314-5

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