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Recommender System Based on User's Tweets Sentiment Analysis

Published: 14 October 2020 Publication History

Abstract

With the ever-growing volume of online information, recommender systems have been an effective strategy to overcome such information overload. The utility of recommender systems cannot be overstated, given its widespread adoption in many web applications, along with its potential impact to ameliorate many problems related to over-choice. Nowadays, people from all around the world use social media sites to share information. Twitter, for example, is a social network in which users send, read posts known as ‘tweets’ and interact with different communities. Users share their daily lives, post their opinions on everything such as brands and places. Social influence plays an important role in product marketing. However, it has rarely been considered in traditional recommender systems. In this paper, we present a new paradigm of e-commerce recommender systems, which can utilize information in social networks. In this study, we have combined sentiment analysis of twitter data with the collaborative filtering in order to increase system accuracy. The proposed system uses lexical approach to analyze sentiment. In order to design the recommender system, we have replaced the missing values of the ratings matrix with the averages of the ratings assigned to the items, to solve the sparsity and cold-start problems inherent in collaborative filtering. The results show that our proposed method improves CF performance. In this experiment we demonstrate how relevant social media can be for recommender systems.

References

[1]
Malone, T. W, Grant, K. R et Turbak, F. A, 1986. The information lens : an intelligent system for information sharing in organizations, ACM.
[2]
D. Goldberg, D. Nichols, B. M. Oki et D. Terry. 1992. Using collaborative filtering to weave an information tapestry, Communications of the ACM, p. 61–71.
[3]
Resnick, P., Iacovou, N., Suchak, M. et Bergstrom, P., 1994. Grouplens : an open architecture for collaborative filtering of netnews, In Proceedings of the 1994 acm conference on computer supported cooperative work, p. 175–186.
[4]
Shardanand, U. et Maes, P., 1995. Social information filtering : algorithms for automating" word of mouth", In Chi, vol. 95, p. 210–217.
[5]
Linden,G., Smith,B. et York,J., 2003. Amazon.com recommendations: Item to item collaborative filtering, IEEE Internet computing, p. 76–80.
[6]
Bell, R. M., Koren, Y. et Volinsky, C., 2007. The bellkor solution to the netflix prize,» KorBell Team's Report to Netflix.
[7]
Kodakateri Pudhiyaveetil, A., Luong, H., Gauch, S. et Eno, J., 2009. Conceptual Recommender System for CiteSeerx, Proceedings of the Third ACM Conference on Recommender Systems - RecSys ’09.
[8]
Barile, F., Ricci, F., Tkalcic, M., Magnini, B., Zanoli, R., Lavelli, A. et Speranza, M., 2019. A News Recommender System for Media Monitoring., IEEE/WIC/ACM International Conference on Web Intelligence, pp. 132-140, October.
[9]
P. Melville et V. Sindhwani, 2017. Recommender systems,» Encyclopedia of Machine Learning and Data Mining, p. 1056–1066.
[10]
Nasukawa, T. et Yi, J., 2003. Sentiment analysis : Capturing favorability using natural language processing, In Proceedings of the 2nd international conference on knowledge capture, p. 70–77.
[11]
Hatzivassiloglou, V. et McKeown, K. R. Predicting the semantic orientation of adjectives, In Proceedings of the 35th annual meeting of the association for computational linguistics and eighth conference of the european chapter of the association for computational linguistics, pp. 147-181.
[12]
Pang, B., Lee, L. et 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, vol. 10, p. 79–86.
[13]
Sindhwani,V. et Melville,P., «Document-wordco-regularization for semi supervised sentiment analysis,» In 2008 eighth ieee international conference on data mining, p. 1025–1030.
[14]
Zhang,D., Xu,H., Su,Z et Xu,Y, 2015. Chinese comments sentiment classification based on word2vec and svmperf,» Expert Systems with Applications, p. 1857–1863.
[15]
Liu, Y., Huang, K., Bao, J. et Chen, K., 2019. Listen to the voices from home : An analysis of chinese tourists’ sentiments regarding australian destinations, Tourism Management, p. 337–347.
[16]
Go, A., Bhayani, R. et Huang, L. 2009. Twitter sentiment classification using distant supervision, CS224N Project Report, Stanford.
[17]
Barbosa,L. et Feng,J. Robust sentiment detection on twitter from biased and noisy data, In Proceedings of the 23rd international conference on computational linguistics : posters, p. 36–44.
[18]
Lampos,V. et Cristianini,N, 2010. Tracking the flu pandemic by monitoring the social web, In 2010 2nd international workshop on cognitive information processing, p. 411–416.
[19]
Bollen, J., Mao, H. et Zeng, X., 2011. Twitter mood predicts the stock market,» Journal of computational science, p. 1–8.
[20]
Mittal, A. et Goel, A. 2012. «Stock prediction using twitter sentiment analysis,» Standford University.
[21]
Smailovic,J., Grear,M., Lavrac,N. et Znidarsic,M. 2013. Predictive sentiment analysis of tweets : A stock market application, In International workshop on human-computer interaction and knowledge discovery in complex, unstructured, big data, p. 77–88.
[22]
Pagolu, V. S., Reddy, K. N., Panda, G. et Majhi, B, 2016. Sentiment analysis of twitter data for predicting stock market movements, In 2016 international conference on signal processing, communication, power and embedded system (scopes), p. 1345–1350.
[23]
Rui, H., Liu, Y. et Whinston, A. 2013. Whose and what chatter matters? the effect of tweets on movie sales,» DecisionSupportSystems, p. 863–870.
[24]
Verma, A., Singh, K. et Kanjilal, K., 2015. Knowledge discovery and twitter sentiment analysis :Mining public opinion and studying its correlation with popularity of indian movies, Int. J. Manage, p. 697–705.
[25]
Munjal,P., Narula,M., Kumar, S. et Banati,H., 2018. Twitter sentimentsbased suggestive framework to predict trends,» Journal of Statistics and Management Systems, p. 685–693.
[26]
Kohavi, R., Longbotham, R., Sommerfield, D. et Henn, 2009. Controlled experiments on the web : survey and practical guide., Data mining and knowledge discovery, p. 140–181.

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cover image ACM Other conferences
ICEEG '20: Proceedings of the 4th International Conference on E-Commerce, E-Business and E-Government
June 2020
139 pages
ISBN:9781450388030
DOI:10.1145/3409929
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 October 2020

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Author Tags

  1. Collaborative filtering
  2. Recommender system
  3. Sentiment analysis, Twitter
  4. Tweets

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Cited By

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  • (2025)Integrated sentiment analysis with BERT for enhanced hybrid recommendation systemsExpert Systems with Applications10.1016/j.eswa.2024.125533261(125533)Online publication date: Feb-2025
  • (2024)Edge-cloud computing oriented large-scale online music education mechanism driven by neural networksJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-023-00555-y13:1Online publication date: 7-Mar-2024
  • (2024)Integrating sentiment features in factorization machines: Experiments on music recommender systemsProceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3627043.3659561(183-188)Online publication date: 22-Jun-2024
  • (2024)A Systematic Literature Review on AI-Based Recommendation Systems and Their Ethical ConsiderationsIEEE Access10.1109/ACCESS.2024.345105412(121223-121241)Online publication date: 2024
  • (2023)Book Reckon - The Use of Virtual Reality in the Creation of Libraries of the Future2023 International Conference on Innovations in Intelligent Systems and Applications (INISTA)10.1109/INISTA59065.2023.10310470(1-6)Online publication date: 20-Sep-2023
  • (2022)RETRACTED ARTICLE: Customer centric hybrid recommendation system for E-Commerce applications by integrating hybrid sentiment analysisElectronic Commerce Research10.1007/s10660-022-09630-z23:1(279-314)Online publication date: 29-Oct-2022
  • (2021)Improving Recommender Systems by Using Time-Weighted Sentiment AnalysisProceedings of the 5th International Conference on E-Commerce, E-Business and E-Government10.1145/3466029.3466057(15-19)Online publication date: 28-Apr-2021

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