Reputation based online product recommendations

Reputation based online product recommendations

Chiranjeevi PandiTeja Santosh Dandibhotla Vishnu Vardhan Bulusu

ACE Engineering College, Telangana State, India

Sreenidhi Institute of Science and Technology, Yamnampet, Ghatkesar,Telangana State, India

JNTUHCEM, Manthani, Telangana State, India

Corresponding Author Email:
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31 December 2017
| Citation



Crucial data namely product aspects and opinions are extracted from online product reviews. The obtained opinions are further analyzed for orientations. These orientations that are positive, negative or neutral are counted to determine the sentiment of the aspect. The sentiments are often turned (unforeseen rise or fall) and due to this the quality of recommended products by the recommendation system is less. The purpose of this study is to assess the importance of aspects reputations in the similarity based product recommendations. A simulation model was established through the analysis of product reviews for ranking the aspects and identifying the frequent aspects among them. The case based reasoning of the searched product against the available similar products from the category are finally compared on the basis of aspect reputations. This comparison provides the list of sorted reputed products in the decreasing order of similarity as recommendations. Through this study, it was found that the recall measure calculated on the reputation based recommendations is better than sentiment based recommendations. The findings of this research are promising in terms of product recommendations using reputation.


product aspects, opinions, aspect rank, frequent aspects, aspect reputation, product similarity, product recommendations

1. Introduction
2. Related works
3. Online product recommendations using statistical reputations
4. Results and discussion
5. Conclusion and future work

Abdel-Hafez A., Xu Y., Tjondronegoro D. (2012). Product reputation model: an opinion mining based approach. SDAD 2012 The 1st International Workshop on Sentiment Discovery from Affective Data.

Bjørkelund E., Burnett T. H. (2012). Temporal Opinion Mining. Master thesis.

Chen G., Chen L. (2014). Recommendation based on contextual opinions. International Conference on User Modeling, Adaptation, and Personalization, pp. 61-73.

Cruz F. L., Troyano J. A., Enriquez F., Ortega F. J. (2013). ‘Long autonomy or long delay?’ The importance of domain in opinion mining. Expert Systems with Applications, Vol. 40, No. 8, pp. 3174-3184.

Dong R., O’Mahony M. P., Schaal M., McCarthy K., Smyth B. (2016). Combining similarity and sentiment in opinion mining for product recommendation. Journal of Intelligent Information Systems, Vol. 46, No. 2, pp. 285-312.

Dong R., Schaal M., O’Mahony M. P., McCarthy K. (2013). Opinionated product recommendation. International Conference on Case-Based Reasoning. Springer Berlin Heidelberg, pp. 44-58.

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

Fahrni A., Klenner M. (2008). Old wine or warm beer: Target-specific sentiment analysis of adjectives. Proc. of the Symposium on Affective Language in Human and Machine.

Fazelpour A. (2016). Investigating the variation of ensemble size on bagging-based classifier performance in imbalanced bioinformatics datasets. Information Reuse and Integration (IRI), 2016 IEEE 17th International Conference on.

Gârbacea C., Tsagkias M., Rijke D. M. (2014). Detecting the Reputation Polarity of Microblog Posts. ECAI.

Ghose A., Ipeirotis P. G., Li B. (2012). Designing ranking systems for hotels on travel search engines by mining user-generated and crowdsourced content. Marketing Science, Vol. 31, No.3, pp. 493-520.

Guo J. L., Peng J. E., Wang H. C. (2013). An opinion feature extraction approach based on a multidimensional sentence analysis model. Cybernetics and Systems, Vol. 44, No. 5, pp. 379-401. 

Gurini D. F., Gasparetti F., Micarelli A., Sansonetti G. (2015). Analysis of sentiment communities in online networks. Proceedings of the International Workshop on Social Personalization & Search.

Hatzivassiloglou V., McKeown K. R. (1997). Predicting the semantic orientation of adjectives. Proceedings of the eighth conference on European chapter of the Association for Computational Linguistics, pp. 174-181.

Hu M., Liu B. (2004). Mining and summarizing customer reviews. Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 168-177.

Hu N., Bose I., Koh N. S., Liu L. (2012). Manipulation of online reviews: An analysis of ratings, readability, and sentiments. Decision Support Systems, Vol. 52, No. 3, pp. 674-684.

Jiao J., Zhou Y. (2011). Sentiment polarity analysis based multi-dictionary. Physics Procedia, Vol. 22, pp. 590-596.

Kim S. M., Hovy E. (2004). Determining the sentiment of opinions. Proceedings of the 20th international conference on Computational Linguistics. 

Lafferty J., McCallum A., Pereira F. C. N. (2001). Conditional random fields: Probabilistic models for segmenting and labeling sequence data. Departmental Papers (CIS).

Li X., Wang H., Yan X. (2015). Accurate recommendation based on opinion mining. Genetic and Evolutionary Computing. Springer International Publishing, pp. 399-408.

Liu L., Özsu M. T. (2009). Encyclopedia of Database Systems. 

Liu Q., Liu B., Zhang Y., Kim D. S., Gao Z. (2016). Improving opinion aspect extraction using semantic similarity and aspect associations. AAAI. 

Miller G. A., Beckwith R., Fellbaum C., Gross D., Miller K. J. (1990). Introduction to WordNet: An on-line lexical database. International journal of lexicography, Vol. 3, No. 4, pp. 235-244.

Mohammad S., Dunne C., Dorr B. (2009). Generating high-coverage semantic orientation lexicons from overtly marked words and a thesaurus. Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, Vol. 2, pp. 599-608.

Pan L. Y., Chiou J. S. (2011). How much can you trust online information? Cues for perceived trustworthiness of consumer-generated online information. Journal of Interactive Marketing, Vol. 25, No. 2, pp. 67-74.

Pang B., Lee L. (2008). Opinion mining and sentiment analysis. Foundations and Trends® in Information Retrieval, Vol. 2, No. 1-2, pp. 1-135.

Poria S., Cambria E., Gelbukh A. (2016). Aspect extraction for opinion mining with a deep convolutional neural network. Knowledge-Based Systems, Vol. 108, pp. 42-49.

Qiu G., He X., Zhang F., Shi Y., Bu J., Chen C. (2010). DASA: dissatisfaction-oriented advertising based on sentiment analysis. Expert Systems with Applications, Vol. 37, No. 9, pp. 6182-6191.

Rada R., Mili H., Bicknell E., Blettner M. (1989). Development and application of a metric on semantic nets. IEEE transactions on systems, man, and cybernetics, Vol. 19, No. 1, pp. 17-30.

Read J., Carroll J. (2009). Weakly supervised techniques for domain-independent sentiment classification. Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion. 

Samha A. K., Li Y., Zhang J. (2014). Aspect-based opinion extraction from customer reviews. Computer Science.

Santosh D. T., Vishnu B. V., Ramesh D. (2016). Extracting product features from reviews using Feature Ontology Tree applied on LDA topic clusters. Advanced Computing (IACC).

Toutanova K., Klein D., Manning C. D., Singer Y. (2003). Feature-rich part-of-speech tagging with a cyclic dependency network. Proceedings of HLT-NAACL, pp. 173-180.

Turney P. D. (2002). Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, pp. 417-424.

Wang W., Wang H. (2015). Opinion-enhanced collaborative filtering for recommender systems through sentiment analysis. New Review of Hypermedia and Multimedia, Vol. 21, No. 3-4, pp. 278-300.

Wu J., Xu B., Li S. (2011). An unsupervised approach to rank product reviews. Fuzzy Systems and Knowledge Discovery (FSKD), 2011 Eighth International Conference on, Vol. 3.

Xu K., Liao S. S., Li J., Song Y. (2011). Mining comparative opinions from customer reviews for Competitive Intelligence. Decision support systems, Vol. 50, No. 4, pp. 743-754.