Big Data Analysis in Commercial Social Networks: Analysis of Twitter Reviews for Marketing Decision Making



Abstract Views
295

Downloads
239

Citations

Share

##plugins.themes.bootstrap3.article.sidebar##

Submitted Feb 22, 2023
Published May 11, 2023
10.24018/compute.2023.3.2.94

Abstract viewed = 295 times

Table of Contents

##plugins.themes.bootstrap3.article.main##

Content generated by users on commercial social networks about products and brands generates large volumes of data that can be transformed into relevant and useful recommendations for marketing decisions. Every day, consumers post their opinions online on social networks about products they have purchased and used, and companies are increasingly interested in tracking this information in real time for better decision making. The main problem is to extract key information from consumers' textual comments and use it automatically to measure the quality of products or brands. In this work, we propose a hybrid approach to automatically analyze these reviews, assigning a quantitative score to negative and positive user content.

The analysis of online consumer sentiment has increased significantly in recent years, being crucial to determine the success of businesses in a wide range of sectors, tourism, hospitality and e-commerce. In the same context, this work proposes a framework for analyzing the sentiment of reviews posted on the Twitter network towards products and brands. The first step is the construction of a dataset by collecting a set of reviews posted online on Twitter, processing and cleaning the textual data for better accuracy, and then developing a hybrid approach for product evaluation and polarities creation using lexicon-based methods and machine learning-based analysis techniques.

Keywords: Bag-of-words representation decision making machine learning online reviews sentiment analysis

References

  1. Sivarajah U, Kamal M, Irani Z, Weerakkody V. Critical analysis of big data challenges and analytical methods. Journal of Business Research. 2017; 70: 263-286.
     Google Scholar
  2. Lytras M, Raghavan V, Damiani E. Cognitive computing and big data analytics research: From metaphors to value space for collective wisdom in human decision making and smart machines. International Journal on Semantic Web and Information Systems. 2017; 13(1): 1-10.
     Google Scholar
  3. Chen H, Chiang R, Storey V. Business intelligence and analytics: From big data to big impact. Management Information Systems Quarterly. 2012; 36(4): 1165-1188.
     Google Scholar
  4. Kumar A, Shankar R, Aljohani N. A big data driven framework for demand driven forecasting with effects of marketing-mix variables. Industrial Marketing Management. 2019
     Google Scholar
  5. Amado A, Cortez P, Rita P, Moro S. Research trends on big data in marketing: A text mining and topic modeling based literature analysis. European Research on Management and Business Economics. 2018; 24(1): 1-7.
     Google Scholar
  6. Zhang K, Katona Z. Contextual advertising. Marketing Science. 2012; 31(6): 980-994.
     Google Scholar
  7. Chuang SH. Co-creating social media agility to build strong customer-firm relationships. Industrial Marketing Management. 2019.
     Google Scholar
  8. De Vries L, Gensler S, Leeflang P. Popularity of brand posts on brand Fan pages: An investigation of the effects of social media marketing. Journal of Interactive Marketing. 2012; 26(2): 83-91.
     Google Scholar
  9. Wang Z, Fujita H, Liu S. Towards felicitous decision making: An overview on challenges and trends of big data. Information Sciences. 2016; 367: 747-765.
     Google Scholar
  10. Feldman R. Techniques and applications for sentiment analysis. Communications of the ACM. 2013; 56(4): 82-89.
     Google Scholar
  11. Liu B. Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies. 2012; 5(1): 1-167.
     Google Scholar
  12. Netzer O, Feldman R, Goldenberg J, Fresko M. Mine your own business: Market-structure surveillance through text mining. Marketing Science. 2012; 31(3): 521-543.
     Google Scholar
  13. Homburg C, Ehm L, Artz M. Measuring and managing consumer sentiment in an online community environment. Journal of Marketing Research. 2015; 52(5): 629-641.
     Google Scholar
  14. Liu X, Burns A C, Hou Y. An Investigation of Brand-Related User-Generated Content on Twitter. Journal of Advertising. 2017; 46(2): 236-247.
     Google Scholar
  15. Day M, Wang C, Chen C, Yang S . Exploring review spammers by re- view similarity: A case of fake review in Taiwan. Proceedings of the third international conference on electronics and software science (ICESS2017), pp.166, 2017.
     Google Scholar
  16. Nitin J, Bing L. Review spam detection. In Proceedings of the 16th international conference on World Wide Web, 2007, pp.1189-1190.
     Google Scholar
  17. Eileen F, Joan B, Tommaso F. Automatic detection of verbal deception. Synthesis Lectures on Human Language Technologies. 2015; 8(3): 1-119.
     Google Scholar
  18. Myle O, Yejin C, Jeffrey T. Finding deceptive opinion spam by any stretch of the imagination. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human LanguageTechnologies. 2011; 1: 309-319.
     Google Scholar
  19. Yla R, James W. The psychological meaning of words: Liwc and computerized text analysis methods. Journal of Language and Social Psychology. 2010; 29(1): 24-54.
     Google Scholar
  20. Arjun M, Vivek V, Bing L, Natalie S. What Yelp fake review filter might be doing?. ICWSM, 2013: 418.
     Google Scholar
  21. Yuejun Li, Xiao F, Shuwu Z. Detecting fake reviews utilizing semantic and emotion model. In 3rd International Conference on Information Science and Control Engineering (ICISCE), 2016, pp. 317-320.
     Google Scholar
  22. Rupesh K, Dewang, Singh AK. Identification of fake reviews using new set of lexical and syntactic features. In Proceedings of the Sixth International Conference on Computer and Communication Technology, 2015, pp. 115-119.
     Google Scholar
  23. Snehasish B, Alton C, Jung-Jae K. Using supervised learning to classify authentic and fake online reviews. In Proceedings of the 9th International Conference on Ubiquitous Information Management and Communication, 2015, pp. 88.
     Google Scholar
  24. Nitin J, Bing L. Opinion spam and analysis. In Proceedings of the International Conference on Web Search and Data Mining, 2008, pp. 219-230.
     Google Scholar
  25. Vladimir V, Steven E, Alex J. Support vector method for function approximation, regression estimation and signal processing. In Advances in neural information processing systems, 1997, pp.281-287.
     Google Scholar
  26. Nir F, Dan G, Moises G. Bayesian network classifiers. Machine Learning.1997; 29(2-3): 131-163.
     Google Scholar
  27. Breiman L, Friedman J, Charles J, Stone, Richard A. Classification and regression trees. CRC press, 1984.
     Google Scholar
  28. Breiman L. Random forests. Machine Learning. 2001; 45(1): 5-32.
     Google Scholar
  29. David R. The regression analysis of binary sequences. Journal of the Royal Statistical Society.1958: 215-242.
     Google Scholar
  30. Fangtao Li, Minlie H, Yang Yi, Xiaoyan Z. Learning to identify review spam. In IJCAI Proceedings-International Joint Conference on Artificial Intelligence, 2011, pp. 2488.
     Google Scholar
  31. Bhadane C, Dalal H, Doshi H. Sentiment analysis: Measuring opinions. Procedia Computer Science. 2015; 45: 808-814.
     Google Scholar
  32. Rahman K, Khamparia A. Techniques, applications and challenges of opinion mining. International Journal of Control Theory and Applications. 2016; 9(41): 455-461.
     Google Scholar
  33. Haddi E, Liu X, Shi Y. The role of text pre-processing in sentiment analysis. Procedia Computer Science. 2013; 17: 26-32.
     Google Scholar
  34. Jandail R. A proposed novel approach for sentiment analysis and opinion mining. International Journal of UbiComp. 2014; 5: 1-10.
     Google Scholar
  35. Demoulin N, Coussement K. Acceptance of text-mining systems: The signaling role of information quality. Information & Management. 2018.
     Google Scholar
  36. Jefferson C, Liu H, Cocea M. Fuzzy approach for sentiment analysis. IEEE international conference on fuzzy systems (FUZZ-IEEE), 2017, pp.1-6.
     Google Scholar
  37. Hung C, Lin H. Using objective words in SentiWordNet to improve word-ofmouth sentiment classification. IEEE Intelligent Systems. 2013; 28: 47-54.
     Google Scholar
  38. Nielsen F. A new evaluation of a word list for sentiment analysis in microblogs. Proceedings of the workshop on ‘making sense of Microposts': big things come in small packages 718 in CEUR workshop proceedings, 2011, pp. 93-98.
     Google Scholar