The Completeness Assessment on Product Reviews based on User-concerned Information Requirements
Authors: 
Chao Li, Hua Zhang, Jing Xiang, Fu LiAuthors Info & Claims
Chao Li, Hua Zhang, Jing Xiang, Fu LiAuthors Info & ClaimsArticle No.: 39, Pages 1 - 5
Abstract
More and more stakeholders such as consumers, sales clerks, manufacturers and administrative staffs are gravitated to wade through product reviews prior to making decisions, and they often try to get more helpful concerned information with a few reviews. However, the information items included in different reviews are often limited and different users often concern different aspects, and the existing research does not achieve the completeness assessment of reviews in the form of information items. Therefore, we identify the user-concerned information items from the reviews published or clicked by a user, and develop the assessment models to assess the completeness of reviews based on the identified information items, and finally show a method for ranking review with the requirements in information completeness.
References
[1]
Ghose, A., & Ipeirotis, P. G. (2011). Estimating the helpfulness and economic impact of product reviews: Mining text and reviewer characteristics. IEEE Transactions on Knowledge and Data Engineering, 23(10), 1498--1512.
[2]
Zhang, J. Q., Craciun, G., & Shin, D. (2010). When does electronic word-of-mouth matter? A study of consumer product reviews. Journal of Business Research, 63(12), 1336--1341.
[3]
Zhang, K. Z., Cheung, C. M., & Lee, M. K. (2014). Examining the moderating effect of inconsistent reviews and its gender differences on consumers' online shopping decision. International Journal of Information Management, 34(2), 89--98.
[4]
Park, S., & Nicolau, J. L. (2015). Asymmetric effects of online consumer reviews. Annals of Tourism Research, 50, 67--83.
[5]
Yan, Z., Xing, M., Zhang, D., & Ma, B. (2015). EXPRS: An extended pagerank method for product feature extraction from online consumer reviews. Information & Management, 52(7), 850--858.
[6]
Berger, J. (2014). Word of mouth and interpersonal communication: A review and directions for future research. Journal of Consumer Psychology, 24(4), 586--607.
[7]
Huang, A. H., Chen, K., Yen, D. C., & Tran, T. P. (2015). A study of factors that contribute to online review helpfulness. Computers in Human Behavior, 48, 17--27.
[8]
Pan, Y., & Zhang, J. Q. (2011). Born unequal: a study of the helpfulness of user-generated product reviews. Journal of Retailing, 87(4), 598--612.
[9]
Li, M., Huang, L., Tan, C. H., & Wei, K. K. (2013). Helpfulness of online product reviews as seen by consumers: Source and content features. International Journal of Electronic Commerce, 17(4), 101--136.
[10]
Ngo-Ye, T. L., & Sinha, A. P. (2014). The influence of reviewer engagement characteristics on online review helpfulness: A text regression model. Decision Support Systems, 61, 47--58.
[11]
Khan, A., Baharudin, B., & Khan, K. (2011). Sentiment classification using sentence-level lexical based semantic orientation of online reviews. Trends in Applied Sciences Research, 6(10), 1141.
[12]
Kwon, O., Lee, N., & Shin, B. (2014). Data quality management, data usage experience and acquisition intention of big data analytics. International Journal of Information Management, 34(3), 387--394.
[13]
Sharma, R., Nigam, S., & Jain, R. (2014). Mining of product reviews at aspect level. arXiv preprint arXiv:1406.3714.
[14]
Miao, Q., Li, Q., & Dai, R. (2009). AMAZING: A sentiment mining and retrieval system. Expert Systems with Applications, 36(3), 7192--7198.
[15]
Crawford, M., Khoshgoftaar, T. M., Prusa, J. D., Richter, A. N., & Al Najada, H. (2015). Survey of review spam detection using machine learning techniques. Journal of Big Data, 2(1), 23.
[16]
Wang, G., Xie, S., Liu, B., & Yu, P. S. (2012). Identify online store review spammers via social review graph. ACM Transactions on Intelligent Systems and Technology (TIST), 3(4), 61.
[17]
Racherla, P., & Friske, W. (2012). Perceived 'helpfulness' of online consumer reviews: An exploratory investigation across three services categories. Electronic Commerce Research and Applications, 11(6), 548--559.
[18]
Asghar, M. Z., Khan, A., Ahmad, S., & Kundi, F. M. (2014). A review of feature extraction in sentiment analysis. Journal of Basic and Applied Scientific Research, 4(3), 181--186.
[19]
Poria S, Cambria E, Ku L W, et al (2014). A rule-based approach to aspect extraction from product reviews[C]//Proceedings of the second workshop on natural language processing for social media (SocialNLP), 28--37.
[20]
Heydari, A., ali Tavakoli, M., Salim, N., & Heydari, Z. (2015). Detection of review spam: A survey. Expert Systems with Applications, 42(7), 3634--3642.
[21]
Decker, R., & Trusov, M. (2010). Estimating aggregate consumer preferences from online product reviews. International Journal of Research in Marketing, 27(4), 293--307.
[22]
Ekinci, E., Türkmen, H., & Omurca, S. İ. (2017). Multi-word Aspect Term Extraction Using Turkish User Reviews. International Journal of Computer Engineering and Information Technology (IJCEIT), 9, 15--23.
[23]
Zha, Z. J., Yu, J., Tang, J., Wang, M., & Chua, T. S. (2014). Product aspect ranking and its applications. IEEE Transactions on knowledge and data engineering, 26(5), 1211--1224.
[24]
Jeyapriya, A., & Selvi, C. K. (2015). Extracting aspects and mining opinions in product reviews using supervised learning algorithm. In Electronics and Communication Systems (ICECS), 2015-2nd International Conference on (pp. 548--552). IEEE.
[25]
Yu, H. K., Zhang, H. P., Liu, Q., Lv, X. Q., & Shi, S. C. (2006). Chinese named entity identification using cascaded hidden Markov model. JOURNAL-CHINA INSTITUTE OF COMMUNICATIONS, 27(2), 87.
[26]
Fan W, Geerts F, Tang N, et al. Conflict resolution with data currency and consistency[J]. Journal of Data and Information Quality (JDIQ), 2014, 5(1-2), 6.
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October 2019
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Published: 22 October 2019
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CSAE 2019
CSAE 2019: The 3rd International Conference on Computer Science and Application Engineering
October 22 - 24, 2019
Sanya, China
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