Journal of the Korean Society for Library and Information Science (한국문헌정보학회지)

Korean Society For Library And Information Science (한국문헌정보학회)
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Sentiment Analysis and Network Analysis based on Review Text

리뷰 텍스트 기반 감성 분석과 네트워크 분석에 관한 연구

  • 김유미 (연세대학교 영어영문학과) ;
  • 허고은 (연세대학교 문헌정보학과)
  • Received : 2021.07.30
  • Accepted : 2021.08.20
  • Published : 2021.08.31
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Abstract

As review text contains the experience and opinions of the customers, analyzing review text helps to understand the subject. Existing studies either only used sentiment analysis on online restaurant reviews to identify the customers' assessment on different features of the restaurant or network analysis to figure out the customers' preference. In this study, we conducted both sentiment analysis and network analysis on the review text of the restaurants with high star ratings and those with low star ratings. We compared the review text of the two groups to distinguish the difference of the two and identify what makes great restaurants great.

리뷰 텍스트는 이용자들의 경험과 의견이 구체적으로 담겨있어 이를 분석하면 리뷰 대상에 대한 많은 내용을 파악할 수 있다. 이에 따라 리뷰 텍스트에 대해 감성 분석을 진행하여 음식점의 각 요인에 대한 이용자의 평가 등을 파악하는 연구, 네트워크 분석을 통한 이용자들의 선호를 파악하는 연구들이 진행되어왔다. 본 연구에서는 음식점 리뷰 텍스트의 별점 기반 만족도가 높은 음식점과 낮은 음식점을 분석대상으로 선정하여 감성 분석과 네트워크 분석을 통합적으로 수행하였다. 서로 다른 두 집단의 리뷰 텍스트에서 나타나는 차이로 음식점의 특성을 파악하여 좋은 음식점의 기준과 음식점 만족도에 영향을 미치는 주요인을 확인하고자 하였다.

Keywords

References

  1. Kim, Ji-Hyun (2016). A content analysis of public information using language network analysis: focused on contents of twitters of metropolitans. Journal of the Korean BIBLIA Society for library and Information Science, 27(3), 151-171. https://doi.org/10.14699/kbiblia.2016.27.3.151
  2. Kim, Hyunji, Park, Seojeong, Song, Chaemin, & Song, Min (2019). Text mining driven content analysis of social perception on schizophrenia before and after the revision of the terminology. Journal of the Korean Society for Library and Information Science, 53(4), 285-307.
  3. Moon, Ji Young (2021. 3. 17.). Naver, Getting rid of "Star Ratings" on Restaurants and Cafe. "To ease small business owners' troubles". YTN. https://www.ytn.co.kr/_ln/0102_202103171050020731
  4. Ban, Hyun-Jeong & Kim, Hak-Seon (2019a). A study on the tripadvisor review analysis of restaurant recognition in Busan 1: especially concerning english reviews. Culinary Science & Hospitality Research, 25(4), 1-11. https://doi.org/10.20878/CSHR.2019.25.4.001
  5. Ban, Hyun-Jeong & Kim, Hak-Seon (2019b). A study on the online review analysis of restaurant recognition in Busan 2: especially concerning korean reviews. The Journal of Business and Economics, 35(2), 185-207.
  6. So, Jin-Soo & Shin, Pan-Seop (2020). Rating prediction by evaluation item through sentiment analysis of restaurant review. Journal of the Korea Society of Computer and Information, 25(6), 81-89.
  7. Song, Jong Seok & Lee, Soo Won (2001). Automatic construction of positive/negative feature-predicate dictionary for polarity classification of product reviews. Journal of KIISS: Software and Applications, 38(3), 157-168.
  8. Lee, Sangjae, Choeh, Joon Yeon, & Choi, Jinho (2014). The determinant factors affecting economic impact, helpfulness, and helpfulness votes of online. Journal of Information Technology Services, 13(1), 43-55 https://doi.org/10.9716/KITS.2014.13.1.043
  9. Lee, Si Hwan & Lee, Hoon Young (2018). The effects of sentiment expressed in restaurant reviews on perceived usefulness and enjoyment of information. The Tourism Sciences Society of Korea International Conference, 83(0), 113-118.
  10. Jeong, Ji-Na, Jeong, Him-Chan, & Kim, Yong (2017). Analysis of trends on disaster safety information based on language network analysis methods. Journal of the Korean BIBLIA Society for library and Information Science, 28(3), 67-93. https://doi.org/10.14699/kbiblia.2017.28.3.067
  11. Jin, Seol A, Heo, Go Eun, Jeong, Yoo Kyung, & Song, Min (2013). Topic-network based topic shift detection on twitter. Journal of the Korean Society for Information Management, 30(1), 285-302. https://doi.org/10.3743/KOSIM.2013.30.1.285
  12. Heo, Go Eun & Song, Min (2013). Examining the intellectual structure of a medical informatics journal with author co-citation analysis and co-word analysis. Journal of the Korean Society for Information Management, 30(2), 207-225. https://doi.org/10.3743/KOSIM.2013.30.2.207
  13. Aguero-Torales, M., Cobo, M., Herrera-Viedma, E., & Lopez-Herrera, A. (2019). A cloud-based tool for sentiment analysis in reviews about restaurants on TripAdvisor. Procedia Computer Science, 162, 392-399. Available: https://doi.org/10.1016/j.procs.2019.12.002
  14. Bastian M., Heymann S., & Jacomy M. (2009). Gephi: an open source software for exploring and manipulating networks. International AAAI Conference on Weblogs and Social Media.
  15. Blondel, V. D., Guillaume, J. L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of statistical mechanics: theory and experiment, 2008(10), P10008. https://doi.org/10.1088/1742-5468/2008/10/P10008
  16. Gang, Xu (2020). Customer Sentiment Analysis: Take Restaurant Online Reviews as an Example. Asia-pacific Journal of Convergent Research Interchange, 6(6), 25-33.
  17. Gao, S., Tang, O., Wang, H., & Yin, P. (2018). Identifying competitors through comparative relation mining of online reviews in the restaurant industry. International Journal of Hospitality Management, 71, 19-32. Available: https://doi.org/10.1016/j.ijhm.2017.09.004
  18. Hossain, E., Sharif, O., Hoque, M. M., & Sarker, I. H. (2020). SentiLSTM: A Deep Learning Approach for Sentiment Analysis of Restaurant Reviews. arXiv:2011.09684
  19. Hutto, C. & Gilbert, E. (2014). VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text. Proceedings of the International AAAI Conference on Web and Social Media, 8(1), 216-225.
  20. Jo, A-Ra, Kim, Tae-Ho, & Kim, Hak-Seon (2017). Exploratory Study of Restaurant Online Reviews Using Semantic Network Analysis. 한국관광레저학회 학술발표대회, 199-210.
  21. Kang, H., Yoo, S. J., & Han, D. (2012). Senti-lexicon and improved Naive Bayes algorithms for sentiment analysis of restaurant reviews. Expert Systems with Applications, 39(5), 6000-6010. Available: http://doi.org.ssl.access.yonsei.ac.kr10.1016/j.eswa.2011.11.107
  22. Kim, H., Yoon, J. W., & Crowcroft, J. (2012). Network analysis of temporal trends in scholarly research productivity. Journal of Informetrics, 6(1), 97-110. Available: https://doi.org/10.1016/j.joi.2011.05.006
  23. Mathayomchan, B. & Taecharungroj, V. (2020). "How was your meal?" Examining customer experience using Google maps reviews. International Journal of Hospitality Management, 90, 102641. Available: https://doi.org/10.1016/j.ijhm.2020.102641
  24. Milojevic, S., Sugimoto, C., Yan, E., & Ding, Y. (2011). The cognitive structure of Library and Information Science: Analysis of article title words. J. Assoc. Inf. Sci. Technol, 62, 1933-1953. https://doi.org/10.1002/asi.21602
  25. Princeton University (2010). About WordNet. WordNet, Princeton University.
  26. Qiwei Gan, Bo H. Ferns, Yang Yu, & Lei Jin (2017). A Text Mining and Multidimensional Sentiment Analysis of Online Restaurant Reviews. Journal of Quality Assurance in Hospitality & Tourism, 18(4), 465-492, Available: DOI: 10.1080/1528008X.2016.1250243
  27. Shuang, K., Zhang, Z., Guo, H., & Loo, J. (2018). A sentiment information Collector-Extractor architecture based neural network for sentiment analysis. Information Sciences, 467, 549-558. Available: https://doi.org/10.1016/j.ins.2018.08.026
  28. Song, M., Heo, G. E., & Lee, D. (2015). Identifying the landscape of alzheimer's disease research with network and content analysis. Scientometrics, 102(1), 905-927. Available: https://doi.org/10.1007/s11192-014-1372-x
  29. Tian, G., Lu, L., & McIntosh, C. (2021). What factors affect consumers' dining sentiments and their ratings: Evidence from restaurant online review data. Food Quality and Preference, 88, 104060. Available: https://doi.org/10.1016/j.foodqual.2020.104060
  30. Yu Boya, Zhou Jiaxu, Zhang Yi, & Cao Yunong (2017). Identifying Restaurant Features via Sentiment Analysis on Yelp Reviews. arXiv:1709.08698
  31. Zhang, Z., Ye, Q., Zhang, Z., & Li, Y. (2011). Sentiment classification of Internet restaurant reviews written in Cantonese. Expert Systems with Applications, 38(6), 7674-7682. Available: https://doi.org/10.1016/j.eswa.2010.12.147