Abstract
Social networks build and maintain relationships between individuals. Sentiment analysis is important in social network analysis for extracting user’s interest from product preferences based on reviews to determine whether it is positive, negative or neutral review. Moreover, sentiment analysis is used to predict the sentiment of users on specific service or product received by them. In this paper, a new technique called sentiment-based rating prediction method is proposed for developing a recommendation system in which the newly introduced technique is capable of mining valuable information from social user reviews in order to predict the accurate items liked by people based on their rating. In this model, a sentiment dictionary is used to calculate the sentiments of individual users on an item. Moreover, reputations of items are computed based on the three sentiments to predict and provide accurate recommendations. In order to increase the accuracy of the outcome, the n-gram methodology is added as a new feature in syntax and semantic analysis along with support vector machines for effective classification of social media data. The main advantage of the proposed model is that it considers semantics and sentiments to predict user interest and hence provides more accurate recommendations.
References
Lei X, Qian X, Zhao G (2016) Rating prediction based on social sentiment from textual reviews. IEEE Trans Multimed 18:595–601
Zhao G, Qian X, Xie X (2016) User-service rating prediction by exploring social users rating behaviors. IEEE Trans Multimed 18:496–506
Cai Y, Leung HF, Tang J, Li J (2016) Typicality-based collaborative filtering recommendation. IEEE Trans Knowl Data Eng 26:766–779
Qian X, Feng H, Zhao G, Mei T (2014) Personalized recommendation combining user interest and social circle. IEEE Trans Knowl Data Eng 26:1763–1777
Blei DM, Jordan MI (2003) Latent dirichlet allocation. J Mach Learn Res 3:993–1022
Liu B (2012) Sentiment analysis and opinion mining. Synth Lect Hum Lang Technol 5:1–167
Chen X, Vorvoreanu M, Madhavan K (2014) Mining social media data for understanding students’ learning experiences. IEEE Trans Learn Technol 7:246–259
Bharat AV, Murthy KS (2016) Exploitation of sentiment analysis in twitter data utilizing machine learning techniques. Int J Res Comput Commun Technol 5:595–601
Jiang S, Qian X, Shenn J, Mei T (2015) Author topic model-based collaborative filtering for personalized POI recommendations. IEEE Trans Multimed 17:907–918
Selvi M, Thangaramya K, Saranya MS, Kulothungan K, Ganapathy S, Kannan A (2019) Classification of medical dataset along with topic modeling using LDA. In: Nath V, Mandal J (eds) Nanoelectronics Circuits and Communication Systems, vol 511. Lecture Notes in Electrical Engineering. Springer, Singapore, pp 1–11
Velvizhy P, Kannan A, Abayambigai S, Sindhuja AP (2016) Food recognition and calorie estimation using multi-class SVM classifier. Asian J Inf Technol 15:866–875
Fu X, Liu W, Xu Y, Cui L (2017) Combine HowNet lexicon to train phrase recursive autoencoder for sentence-level sentiment analysis. Neurocomputing 241:8–27
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Munuswamy, S., Saranya, M.S., Ganapathy, S. et al. Sentiment Analysis Techniques for Social Media-Based Recommendation Systems. Natl. Acad. Sci. Lett. 44, 281–287 (2021). https://doi.org/10.1007/s40009-020-01007-w
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DOI: https://doi.org/10.1007/s40009-020-01007-w