Abstract
Online Social Networks is a place where a user is truly free to express himself or herself, and it is observed to be so. Social networks are used by users for not only socializing but to buy or sell products. This user behavior is quite adamant from the type of comments they post on different social networks. The word recommendation system is still not personalized rather generalized for all different websites. It can, however, be personalized by the use of sentiment analysis and the model here has done the same here. The model has used subjectivity and polarity for making a personalized recommender system by analyzing the behavior and classifying them. This provided the model with two greater ways to recommend, i.e., recommending on the basis of the user as prevalent from their comments and also by the topics been discussed by them.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Zhang, Y., Liu, R.F., Li, A.Z.: A novel approach to recommender system based on aspect-level sentiment analysis. In: 4th National Conference on Electrical, Electronics and Computer Engineering (NCEECE 2015)
Pazzani M.J., Billsus D.: Content-based recommendation systems. In: Brusilovsky P., Kobsa A., Nejdl W. (eds) The Adaptive Web. Lecture Notes in Computer Science, vol 4321. Springer, Berlin, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72079-9_10
Du, Q., Zhu, D., Duan, W.: Recommendation system with aspect-based sentiment analysis (2018). http://ceur-ws.org/Vol-1520/paper29.pdf
Naw, N., Hlaing, E.E.: Relevant Words Extraction Method for Recommendation System. University of Technology (Yatanarpon Cyber City) and University of Computer Studies (Taung Ngu)
Halvey, M., Keane, M.T.: An assessment of tag presentation techniques. In: Archived 2017-05-14 at the Wayback Machine
Qiu, M., Li, F., Wang, S., Gao, X., Chen, Y., Zhao, W., Chen, H., Huang, J., Chu, W.: AliMe Chat: A Sequence to Sequence and Rerank based Chatbot Engine. Alibaba Group, Hangzhou, China
https://github.com/jbencina/facebook-news (March–May 2019)
Dong, Y., Tang, J., Wu, S., Tian, J., Chawla, N.V., Rao, J., Cao, H.: Link Prediction and Recommendation across Heterogeneous Social Networks. In: IEEE 12th International Conference on Data Mining, pp. 181–190 (2012)
Thies, I.M., Menon, N., Magapu, S., Subramony, M., O’Neill, J.: How do you want your Chatbot? An Exploratory Wizard-of-Oz Study with Young, Urban Indians. Conference paper-20 Sept 2017
Infanta, S.D., Chellammal, P.: A survey on sentiment analysis for product recommendation system using hybrid learning algorithm. Int. J. Res. Sci. Innov. (IJRSI) VI(I) (2019). ISSN 2321–2705
Leung, C.W., Chan, S.C., Chung, F.: Integrating collaborative filtering and sentiment analysis: A rating inference approach. In: ECAI 2006 Workshop on Recommender Systems, pp. 62–66
Gurini, D.F., Gasparetti, F, Micarelli, A., Sansonetti, G.: A sentiment-based approach to Twitter user recommendation. In: RSWeb@RecSys (2013)
Priyadharsini, R.L., Felciah, M.L.P.: Recommendation system in e-commerce using sentiment analysis. Int. J. Eng. Trends Technol. (IJETT) 49(7) (2017)
Ziani, A., Azizi, N., Schwab, D., Aldwairi, M., Chekkai, N., Zenakhra, D., Cheriguene, S.: Recommender system through sentiment analysis. In: 2nd International Conference on Automatic Control, Telecommunications and Signals (Dec 2017), Annaba, Algeria. ffhal-01683511f, https://hal.archives-ouvertes.fr/hal-01683511. 13 Jan 2018
Hassan, A.K.A, Abdulwahhab, A.B.A.: Reviews Sentiment analysis for collaborative recommender system. Kurd. J. Appl. Res. (KJAR) 2(3) (2017). https://doi.org/10.24017/science.2017.3.22. Print-ISSN: 2411-7684–Electronic-ISSN: 2411-7706, kjar.spu.edu.iq
Sarkar, D., Jana, P.: Analyzing user activities using vector space model in online social networks. In: National Conference on Recent Trends in Information Technology and Management (RTITM 2017)
Sarkar, D., Roy, S., Giri, C., Kole, D.K.: A statistical model to determine the behavior adoption in different timestamps on online social network. Int. J. Knowl. Syst. Sci. 10(4) (2019)
Sarkar, D., Debnath, S., Kole, D.K., Jana, P.: Influential nodes identification based on activity behaviors and network structure with personality analysis in egocentric online social networks. Int. J. Knowl. Syst. Sci. 10(4) (2019)
Pandas Documentation (March–June 2019). https://pandas.pydata.org
Manning, C.D., Raghavan, P., Schutze, H.: Scoring, term weighting, and the vector space model. In: Introduction to Information Retrieval. p. 100 (2008). https://doi.org/10.1017/cbo9780511809071.007. ISBNÂ 978-0-511-80907-1
Rajaraman, A., Ullman, J.D.: T: Data Mining. Mining of Massive Datasets (2011)
Zafarani, R., Abbasi, M.A., Liu, H.: T: Social Media Mining, An Introduction. Cambridge University Press, 20 Apr 2014
Medhat, W., Hassan, A., Korashy, H.: Sentiment analysis algorithms and applications: a survey. In: School of Electronic Engineering, Canadian International College, Cairo Campus of CBU, Egypt. Ain Shams University, Faculty of Engineering, Computers & Systems Department, Egypt. Received 8 September 2013; Revised 8 April 2014. Accepted 19 April 2014
Pang, B., Lee, L.: Opinion Mining and Sentiment Analysis (2008). ISBN: 978-1-60198-150-9 c
NLTK 3.4 documentation (March–June 2019). http://www.nltk.org/
Koukourikos, A., Stoitsis, G., Karampiperis, P.: Sentiment Analysis: A tool for Rating Attribution to Content in Recommender Systems
Pandas Documentation (March–June 2019). http://danielhnyk.cz/limitations-of-pandas-0-18-1-hdfstore/
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Kundu, S.S., Desai, K., Ghosh, S., Sarkar, D. (2021). Personalized Word Recommendation System Using Sentiment Analysis. In: Bhattacharjee, D., Kole, D.K., Dey, N., Basu, S., Plewczynski, D. (eds) Proceedings of International Conference on Frontiers in Computing and Systems. Advances in Intelligent Systems and Computing, vol 1255. Springer, Singapore. https://doi.org/10.1007/978-981-15-7834-2_8
Download citation
DOI: https://doi.org/10.1007/978-981-15-7834-2_8
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-7833-5
Online ISBN: 978-981-15-7834-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)