Aspect Βased Classification Model for Social Reviews

J. Mir, A. Mahmood, S. Khatoon

Abstract


Aspect based opinion mining investigates deeply, the emotions related to one’s aspects. Aspects and opinion word identification is the core task of aspect based opinion mining. In previous studies aspect based opinion mining have been applied on service or product domain. Moreover, product reviews are short and simple whereas, social reviews are long and complex. However, this study introduces an efficient model for social reviews which classifies aspects and opinion words related to social domain. The main contributions of this paper are auto tagging and data training phase, feature set definition and dictionary usage. Proposed model results are compared with CR model and Naïve Bayes classifier on same dataset having accuracy 98.17% and precision 96.01%, while recall and F1 are 96.00% and 96.01% respectively. The experimental results show that the proposed model performs better than the CR model and Naïve Bayes classifier.


Keywords


POS; Chunking; Word Case; Feature Set; Dictionary; NER; IOB tagging

Full Text:

PDF

References


B. Liu, Sentiment analysis and opinion mining, Synthesis lectures on human language technologies Vol. 5, Morgan & Claypool, 2012

J. Mir, M. Usman, “An effective model for aspect based opinion mining for social reviews,” Tenth International Conference on Digital Information Management, pp. 49-56, 2015

[3] T. Chinsha, S. Joseph, “A syntactic approach for aspect based opinion mining,” IEEE International Conference on Semantic Computing, pp. 24-31, 2015

[4] I. Penalver-Martinez, F. Garcia-Sanchez, R. Valencia-Garcia, M. A. Rodríguez-García, V. Moreno, A. Fraga, J. L. Sanchez-Cervantes, “Feature-based opinion mining through ontologies”, Expert Systems with Applications, Vol. 41, No. 13, pp. 5995-6008, 2014

A. Bagheri, M. Saraee, F. De Jong, “Care more about customers: unsupervised domain-independent aspect detection for sentiment analysis of customer reviews”, Knowledge-Based Systems, Vol. 52, pp. 201-213, 2013

A. Bagheri, M. Saraee, F. De Jong, “ADM-LDA: An aspect detection model based on topic modelling using the structure of review sentences”, Journal of Information Science, Vol. 40, No. 5, pp. 621-636, 2014

F. Tian, F. Wu, K.-M. Chao, Q. Zheng, N. Shah, T. Lan, J. Yue, “A topic sentence-based instance transfer method for imbalanced sentiment classification of Chinese product reviews”, Electronic Commerce Research and Applications, Vol. 16, pp. 66-76, 2015

C. Quan, F. Ren, “Unsupervised product feature extraction for feature-oriented opinion determination”, Information Sciences, Vol. 272, pp. 16-28, 2014

M. Zimmermann, E. Ntoutsi, M. Spiliopoulou, “Extracting opinionated (sub) features from a stream of product reviews using accumulated novelty and internal re-organization”, Information Sciences, Vol. 329, pp. 876-899, 2016

A. Mukherjee, B. Liu, “Aspect extraction through semi-supervised modeling”, 50th Annual Meeting of the Association for Computational Linguistics: Long Papers,Vol. 1, pp. 339-348, 2012

Z. Chen, B. Liu, “Mining topics in documents: standing on the shoulders of big data”, 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1116-1125, 2014

L. Zhang, B. Liu, “Aspect and entity extraction for opinion mining”, in Data Mining and Knowledge Discovery for Big Data, pp. 1-40, Springer, 2014

M. Eirinaki, S. Pisal, J. Singh, “Feature-based opinion mining and ranking”, Journal of Computer and System Sciences, Vol. 78, No. 4, pp. 1175-1184, 2012

L. Lizhen, S. Wei, W. Hanshi, L. Chuchu, L. Jingli, “A novel feature-based method for sentiment analysis of Chinese product reviews”, Communications, China, Vol. 11, No. 3, pp. 154-164, 2014

H. Xu, F. Zhang, W. Wang, “Implicit feature identification in Chinese reviews using explicit topic mining model”, Knowledge-Based Systems, Vol. 76, pp. 166-175, 2015

K. Ravi and V. Ravi, “A survey on opinion mining and sentiment analysis: tasks, approaches and applications”, Knowledge-Based Systems, Vol. 89, pp. 14-46, 2015

W. Zhang, H. Xu, W. Wan, “Weakness Finder: Find product weakness from Chinese reviews by using aspects based sentiment analysis”, Expert Systems with Applications, Vol. 39, No. 11, pp. 10283-10291, 2012

D. Kang, Y. Park, “Review-based measurement of customer satisfaction in mobile service: Sentiment analysis and VIKOR approach”, Expert Systems with Applications, Vol. 41, No. 4, Part 1, pp. 1041-1050, 2014

E. Marrese-Taylor, J. D. Velasquez, F. Bravo-Marquez, “A novel deterministic approach for aspect-based opinion mining in tourism products reviews”, Expert Systems with Applications, Vol. 41, No. 17, pp. 7764-7775, 2014

B. Liu, Web data mining: exploring hyperlinks, contents, and usage data, Springer Science & Business Media, 2007

M. Afzaal, M. Usman, “A novel framework for aspect-based opinion classification for tourist places”, Tenth International Conference on Digital Information Management, pp. 1-9, 2015

S. Y. Ganeshbhai, B. K. Shah, “Feature based opinion mining: A survey”, IEEE International Advance Computing Conference, pp. 919-923, 2015

W. Wang, H. Xu, W. Wan, “Implicit feature identification via hybrid association rule mining”, Expert Systems with Applications, Vol. 40, No. 9, pp. 3518-3531, 2013

K. Schouten and F. Frasincar, “Finding Implicit Features in Consumer Reviews for Sentiment Analysis”, in Web Engineering: Springer, 2014, pp. 130-144.

L. Chen, L. Qi, F. Wang, “Comparison of feature-level learning methods for mining online consumer reviews”, Expert Systems with Applications, Vol. 39, No. 10, pp. 9588-9601, 2012

C. Sutton A. McCallum, An introduction to conditional random fields, Now Publishers, 2012

J. Baldridge, “The opennlp project”, url: https://opennlp.apache.org, (accessed 2 February 2012), 2005

T. Kudo, “CRF++: Yet another CRF toolkit”, Software available at http://crfpp. sourceforge. net, 2005




eISSN: 1792-8036     pISSN: 2241-4487