1.Title

소셜미디어의 동영상 리뷰에서 감정이 리뷰 유용성에 미치는 영향에 관한 연구

2. References 

[1] Alsharo, M., Sibona, C., & Alnsour, Y. (2016). Online video reviews helpfulness: Exploratory study. In AMCIS 2016: Surfing the IT Innovation Wave - 22nd Americas Conference on Information Systems.

[2] Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research (3), 993-1022.

[3] Bughin, J., Doogan, J., & Vetvik, O. J. (2010). A New Way to Measure Word-of-Mouth Marketing. McKinsey Quarterly, 2, 113-116.

[4] Chang, Y. I., & Jung, Y. S. (2019). A Study on YouTube Product Review Channel Subscribers’ Product Attitude Formation Process. Global E-Business Association, 20(2), 77-97.

[5] Chatterjee, P. (2001). Online Reviews: Do Consumers Use Them?. Advance in Consumer Research, 28: 129-133.

[6] Chevalier, J. A., & Mayzlin, D. (2006). The Effect of Word of Mouth on Sales: Online Book Reviews. Journal of Marketing Research, 43(3), 345?354.

[7] Dellarocas, C. (2003). The Digitization of Word of Mouth: Promise and Challenges of Online Feedback Mechanisms. Management Science, 49(10), 1407-1424.

[8] Doh, S.J. & Hwang, J.S. (2009) How Consumers Evaluate eWOM (Electronic Word-of-Mouth) Messages. Cyberpsychology & Behavior, 12, 193-197.

[9] Duan, W., Gu, B., & Whinston, A. B. (2008). Do online reviews matter? An empirical investigation of panel data. Decision support systems, 45(4), 1007-1016.

[10] Edwards T. (2015). YouTube ranking factors: getting ranked in the second largest search engine. Retrieved July 24, 2015, Retrieved from http://searchengineland.com/youtube-ranking-factors-getting-ranked-secondlargest-search-engine-225533

[11] Filieri, R. (2015). What makes online reviews helpful? A diagnosticity-adoption framework to explain informational and normative influences in e-WOM. Journal of business research, 68(6), 1261-1270.

[12] Forman, C., Ghose, A., & Wiesenfeld, B. (2008). Examining the relationship between reviews and sales: The role of reviewer identity disclosure in electronic markets. Information systems research, 19(3), 291-313.

[13] Furner, C. P., & Zinko, R. A. (2017). The influence of information overload on the development of trust and purchase intention based on online product reviews in a mobile vs. web environment: an empirical investigation. Electronic Markets, 27, 211-224.

[14] Harrison-Walker, L. (2001). The measurement of word-of-mouth communication and an investigation of service quality and customer commitment as potential antecedents. Journal of Service Research, 4(1), 60-75.

[15] Hong, T., Niu, H., Ren, G., & Park, J. (2018). Multiple-Topic Sentiment Analysis Using LDA for Online Review. Journal of Information Systems, 27(1), 89-110.

[16] Jarboe. G. (2015). 71% of Companies Plan to Increase their Video Marketing Budget - Because it Works. Retrieved November 25, 2015, Retrieved from http://www.reelseo.com/increase-video-marketingspend/#ixzz41NiaUbKU

[17] Kim, Ah-Reum, & Kang, Hyunjeong. (2016). The Effect of Consumer Characteristics on Exploratory Information Search and Information Use Behavior. Journal of Information Technology Services, 15(1), 19?37.

[18] Kim, G., & Lee, Y. (2021). Suggested solutions to problems based on the concept and characteristics of live commerce. Journal of the Korean Society for Knowledge and Information Technology, 16(6), 1333-1340.

[19] Kumar, N. & Benbasat, I. (2006) Research Note: The Influence of Recommendations and Consumer Reviews on Evaluations of Websites. Information Systems Research, 17, 425-439.

[20] Lee, H. A., W. C. Lee and K. J. Lee, Antomatic Product Feature Extraction for Efficient Analysis of Product Reviews Using Term Statistics. The KIPS Transactions : Part B, Vol.16, No.6(2009), 497-502.

[21] Lee, S., & Lee, H. (2017). A study on airline service satisfaction factors using Korean natural language processing, LDA, and sentiment analysis. Proceedings of the 2017 Fall Conference on Management Information Systems, Korean Society of Management Information Systems, 323-330.

[22] Lin, C., & He, Y. (2009, November). Joint sentiment/topic model for sentiment analysis. In Proceedings of the 18th ACM conference on Information and knowledge management (pp. 375-384).

[23] Liu, Z., & Park, S. (2015). What makes a useful online review? Implication for travel product websites. Tourism management, 47, 140-151.

[24] Minjung Roh. (2021). The Effect of Review Quality on Review Helpfulness: The Moderating Role of Review Star ratings. Journal of Digital Contents Society, 22(6), 999-1007.

[25] Mohammad, S. M., & Turney, P. D. (2013). Crowdsourcing a Word-Emotion Association Lexicon. Computational Intelligence, 29(3), 436?465.

[26] Mudambi, Susan & Schuff, David. (2010). What Makes a Helpful Online Review? A Study of Customer Reviews on Amazon.com. MIS Quarterly. 34. 185-200. 10.2307/20721420.

[27] Riegner, Cate. (2007). Word of Mouth on the Web: The Impact of Web 2.0 on Consumer Purchase Decisions. Journal of Advertising Research. 47.

[28] Schindler, R. M., & Bickart, B. (2012). Perceived helpfulness of online consumer reviews: The role of message content and style. Journal of Consumer Behaviour, 11(3), 234-243.

[29] Shah, A. M., & Lee, K. (2022). The Role of Emotions Intensity in Helpfulness of Online Physician Reviews. Intelligent Automation & Soft Computing, 31(3).

[30] Shin, H. W., Lee, S. J., Son, G. J., Kim, H. R., & Kim, Y. (2021). Integrated Verbal and Nonverbal Sentiment Analysis System for Evaluating Reliability of Video Contents. KIPS Transactions on Software and Data Engineering, 10(4), 153-160.

[31] Shin, P. S. (2020). Emotional analysis system for social media using sentiment dictionary with newly-created words. Journal of the Korea Society of Computer and Information, 25(4), 133-140.

[32] Song, J. S. and S. W. Lee, " Automatic Construction of Positive/Negative Feature Predicate Dictionary for Polarity Classification of Product Reviews." Journal of KIISE: Software and Applications, Vol.38, No.3 (2011), 157-168.

[33] Srivastava, V., & Kalro, A. D. (2019). Enhancing the helpfulness of online consumer reviews: the role of latent (content) factors. Journal of Interactive Marketing, 48(1), 33-50.

[34] Susarla, A., Oh, J. H., & Tan, Y. (2012). Social networks and the diffusion of user-generated content: Evidence from YouTube. Information Systems Research (23:1), pp. 23-41.

[35] Taemin Cho, Jee-Hyong Lee. (2015). Latent Keyphrase Extraction Using LDA Model. Journal of Korean Institute of Intelligent Systems, 25(2), 180-185.

[36] Ullah, R., Zeb, A., & Kim, W. (2015). The impact of emotions on the helpfulness of movie reviews. Journal of Applied Research and Technology, 13, 359-363.