A PERSONALIZED SENTIMENT ANALYSIS FRAMEWORK BASED ON THE EX-PLORATION OF HOMOGENEITY THEORY AND TECHNOLOGY

Mohsen Mohammadihadadan ; Hannaoleszkiewicz Hannaoleszkiewicz

VOLUME01ISSUE03

ABSRACT


People have different views on the same subject and thus challenge the traditional sentiment analysis system. Traditional sentiment analysis systems rely on traditional statistical models, which suffer from data scarcity. This paper builds a personalized sentiment analysis system that uses social relationship information to balance personalization and over-provisioning. The method is based on a network lasso that automatically brings together socially nearby users so that users in the same group share the same personalized model. At the same time, a distributed optimization algorithm is designed to make the personalized sentiment analysis system extend to large networks. Experiments on the Yelp review dataset show that our approach is always better than the competition approach.

KEYWORDS


Emotional analysis system, Social relationship information, Personalized model, Network lasso, Optimization algorithm.

REFERENCES


1 . Mohammad Al Boni, Keira Zhou, Hongning Wang, and Matthew S Gerber. 2015. Model adaptation for personalized opinion analysis. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL), pages 769–774.

2. Stephen Boyd, Neal Parikh, Eric Chu, BorjaPeleato, and Jonathan Eckstein. 2011. Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends R in Machine Learning, 3(1):1–122.

3.John S Breese, David Heckerman, and Carl Kadie. 1998. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of Uncertainty in Artificial Intelligence (UAI).

4.Stanley F Chen and Joshua Goodman. 1996. An empirical study of smoothing techniques for language modeling. In Proceedings of the 34th annual meeting on Association for Computational Linguistics, pages 310–318.Association for Computational Linguistics.

5.Alec Go, RichaBhayani, and Lei Huang. 2009. Twitter sentiment classification using distant supervision. CS224N Project Report, Stanford, 1(12).

6.DavidHallac, Jure Leskovec, and Stephen Boyd. 2015. Network lasso: Clustering and optimization in large graphs. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 387–396. ACM.
1https://www.yelp.com/dataset_challenge

7.EfthymiosKouloumpis, Theresa Wilson, and Johanna D Moore. 2011. Twitter sentiment analysis: The good the bad and the omg! Icwsm, 11(538-541):164.

8.Christopher J Leggetter and Philip C Woodland. 1995. Maximum likelihood linear regression for speaker adaptation of continuous density hidden markov models. Computer Speech & Language, 9(2):171–185.

9.Ryan McDonald, Kerry Hannan, Tyler Neylon, Mike Wells, and Jeff Reynar. 2007. Structured models for fine-to-coarse sentiment analysis. In Annual Meeting-Association For Computational Linguistics, volume 45, page 432. Citeseer.

10.Miller McPherson, Lynn Smith-Lovin, and James M Cook. 2001. Birds of a feather: Homophily in social net-works. Annual review of sociology, 27(1):415–444.

11.AlvaroOrtigosa, Jos´ e M Mart´ ın, and Rosa M Carro. 2014. Sentiment analysis in facebook and its application to e-learning. Computers in Human Behavior, 31:527–541.

12.Bo Pang, Lillian Lee, and ShivakumarVaithyanathan. 2002. Thumbs up?: sentiment classification using machine learning techniques. In Proceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10, pages 79–86. Association for Computational Linguistics.

13.Jason D Rennie, Lawrence Shih, Jaime Teevan, David R Karger, et al. 2003. Tackling the poor assumptions of naive bayes text classifiers. In ICML, volume 3, pages 616–623. Washington DC).

14.Kaisong Song, Shi Feng, Wei Gao, Daling Wang, Ge Yu, and Kam-Fai Wong. 2015. Personalized sentiment clas-sification based on latent individuality of microblog users. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pages 2277–2283.

15.SoroushVosoughi, PrashanthVijayaraghavan, and Deb Roy. 2016. Tweet2vec: Learning tweet embeddings using character-level cnnlstm encoder-decoder. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, pages 1041–1044. ACM.

16.H. K. Nguyen, A. Khodaei, and Z. Han, “A Big Data Scale Algorithm for Optimal Scheduling of Integrated Microgrids,” IEEE Transactions on Smart Grid, In press.

AUTHOR’S AFFILIATION


MOHSEN MOHAMMADIHADADAN
Department of Technical and Engineering, South Tehran Branch, Islamic Azad University, Tehran, IRAN.

HANNAOLESZKIEWICZ HANNAOLESZKIEWICZ
Department of Technical and Engineering, South Tehran Branch, Islamic Azad University, Tehran, IRAN.

Scroll to Top