Multi-Feature Segmentation and Cluster based Approach for Product Feature Categorization

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Author(s)

Bharat Singh 1,* Saroj Kushwah 2 Sanjoy Das 2

1. School of Computing Science and Engineering, Galgotias University, India

2. GLA University, India, School of Computing Science and Engineering, Galgotias University, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijitcs.2016.03.04

Received: 22 Jun. 2015 / Revised: 6 Oct. 2015 / Accepted: 17 Dec. 2015 / Published: 8 Mar. 2016

Index Terms

Product feature categorization, irrelevant feature, opinion mining, sentiment orientation, feature, cluster

Abstract

At a recent time, the web has become a valuable source of online consumer review however as the number of reviews is growing in high speed. It is infeasible for user to read all reviews to make a valuable or satisfying decision because the same features, people can write it contrary words or phrases. To produce a useful summary of domain synonyms words and phrase, need to be a group into same feature group. We focus on feature-based opinion mining problem and this paper mainly studies feature based product categorization from the number of users - generated review available on the different website. First, a multi-feature segmentation method is proposed which segment multi-feature review sentences into the single feature unit. Second part of speech dictionary and context information is used to consider the irrelevant feature identification, sentiment words are used to identify the polarity of feature and finally an unsupervised clustering based product feature categorization method is proposed. Clustering is unsupervised machine learning approach that groups feature that have a high degree of similarity in a same cluster. The proposed approach provides satisfactory results and can achieve 100% average precision for clustering based product feature categorization task. This approach can be applicable to different product.

Cite This Paper

Bharat Singh, Saroj Kushwah, Sanjoy Das, "Multi-Feature Segmentation and Cluster based Approach for Product Feature Categorization", International Journal of Information Technology and Computer Science(IJITCS), Vol.8, No.3, pp.33-42, 2016. DOI:10.5815/ijitcs.2016.03.04

Reference

[1]S. Momtazi, S. Kazalski, D. Klakow, “A Combined Query Expansion Technique for Retrieving Opinions from Blogs,” Intelligent Systems Design and Applications, ISDA, Ninth International Conference on, pp. 791-796, 30 Nov 2009. 

[2]Y. Choi, Y. Kim, and S. Myaeng, “Domain-specific sentiment analysis using contextual feature generation, “Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion, ACM, 2009. 

[3]W. J. Jia, S. Zhang, Y.J. Xia, J. Zhang, H. Yu,” A Novel Product Features Categorize Method Based on Twice-Clustering,” Web Information Systems and Mining (WISM), 2010 International Conference on, vol.1, pp.281,284, 23-24 Oct. 2010.

[4]C. L. Fermín, “A knowledge-rich approach to feature-based opinion extraction from product reviews,” Proceedings of the 2nd international workshop on Search and mining user-generated contents, ACM, 2010. 

[5]J. Zhu, H. Wang, M. Zhu, B.K. Tsou, M. Ma, “Aspect-Based Opinion Polling from Customer Reviews,” Affective Computing, IEEE Transactions on, vol.2, no.1, pp. 37-49, 2011.

[6]S. Moghaddam, M. Ester, “AQA: Aspect-based Opinion Question Answering,” Data Mining Workshops (ICDMW), IEEE 11th International Conference on, pp.89-96, 11 Dec. 2011. 

[7]L. Liu, Z. Lv, H. Wang, “Opinion mining based on feature-level,” Image and Signal Processing (CISP),5th International Congress on, pp. 1596,1600, 16-18 Oct. 2012.

[8]X. Yu, Y. Liu, X. Huang A. An, “Mining Online Reviews for Predicting Sales Performance: A Case Study in the Movie Domain,” Knowledge and Data Engineering, IEEE Transactions on, vol.24, no.4, pp. 720-734, April 2012 

[9]Z. Zhai, B. Liu, H. Xu, & P. Jia, Clustering product features for opinion mining, “Proceedings of the fourth ACM international conference on Web search and data mining”, ACM, 2011.

[10]E.Riloff et al “Feature submission for opinion Analysis,” Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp.440-448, 2006.

[11]M.Thomas, B.Pang, L.ee, “Get out the Vote: Determining Support or Opposition from Congressional Floor-Debate Transcripts,” Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp327-335, 2006.

[12]M.Hu and B.Liu, “mining Opinion Feature in Customer Review,” Proceedings of the 9th National Conference on artificial intelligence, 2004.

[13]M.Hu and B.Liu, “Mining and Summarizing Customer Review,” Proceedings of the international Conference on Knowledge Discovery and Data mining, pp. 168-177, 2004.

[14]Hu, Mingqin and Bing Liu, “Minning and Summarizing “In proceeding of KDD, 2004.

[15]J.C. Reynar, “An Automatic method of Finding Topic Boundries,” Proceedings of the 32nd annual Meeting Association for CompuationalLinguistics, pp.331-333, 1994.

[16]P. Fragkou, V.Petridis, and A. Kehagias, “A Dynamic Programming Algorithm for Linear text segmentation,” proceeding on J.Intelligent Information System, Vol.23, no.2, pp.179-197, 2004.

[17]F.Y.Y.Choi, “Advances in Domain Independent Linear Text segmentation,” Proceeding of the 1st Meeting North Am.Chapter Association for Computational linguistic, pp.26-33, 2000.

[18]K.W.church, “Char Align P: A Program for Aliging Parallel Texts at the Charcter Level,” Proceedings of the 31st Ann.meeting Association for computationallinguisics, pp.1-8, 1993.

[19]C.Scaffidi, K.Bierhoff and et al, Red Opal: product feature scoring from reviews, “proceeding of the 8th ACM conference on Electronic Commerce”, pp.182-191, 2007.

[20]A.Popescu and O. Etzioni, “Extarcting Product Feature and Opinions form Reviews,” Proceedings of the Conference on Empirical Methods on Natural Language Processing, pp.339-346, 2006.

[21]Kobyashi, Nozomi, K.Inui and Y.Matsumoto, “Extracting Aspect-Evaluation and aspect of Relation in opinion Mining,” Proceeding of EMNLP, 2007.

[22]Bahram Izadi, Bahram Ranjbarian, Saeedeh Ketabi, Faria Nassiri-Mofakham, “Performance Analysis of Classification Methods and Alternative Linear Programming Integrated with Fuzzy Delphi Feature Selection”, International Journal of Information Technology and Computer Science (IJITCS),Vol. 5, No. 10, September 2013, PP.9-20.

[23]Suneetha Chittineni, Raveendra Babu Bhogapathi, “Determining Contribution of Features in Clustering Multidimensional Data Using Neural Network”, IJITCS Vol. 4, No. 10, September 2012, PP.29-36.