Abstract
Online reviews and their star ratings are an important asset for users in deciding whether to buy a product, watch a movie, or where to plan for a tour. The review text is quite explanatory and expressive where the user shares his experiences in a detailed manner. But the star rating attached to a review is often subjective and at times biased to the reviewer’s personality. The priorities given to different aspects or features by a user might in turn influence his rating. This discrepancy in the review content and the star rating makes a significant impact when sentiment is extracted from large voluminous data. This emphasizes the need to build a model to predict the rating based on the textual content in the reviews rather than bluntly following the star rating attached to the review. A novel weighted textual feature method is applied to assign an appropriate score to each review. Sequential Minimal Optimization (SMO) regression is applied to the feature set to predict the review rating which is based purely on the textual content of the review. The predicted rating has been compared with the actual user rating, and an analysis has been carried out to draw insights into the discrepancy. The model has been experimented in the tourism domain, and the results are quite promising. The regression residual value obtained for hotel and destination data is 0.8772 and 0.9503.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retr. 2.1–2, 1–135 (2008)
Priyanka, C., Gupta D.: Identifying the best feature combination for sentiment analysis of customer reviews. ICACCI 102–108 (2013)
Mishra, D., Venugopalan, M., Gupta, D.: Context specific Lexicon for Hindi reviews. Procedia Comput. Sci. 93, 554–563 (2016)
Sanagar, S., Gupta, D.: Adaptation of multi-domain corpus learned seeds and polarity lexicon for sentiment analysis. Comput. Netw. Commun. (CoCoNet) 50–58 (2015)
Venugopalan, M, Gupta D.: Exploring sentiment analysis on twitter data. In: Eighth International Conference on Contemporary Computing (IC3), pp. 241–247 (2015)
Gupta, N., di Fabbrizio, G., Haffner, P.: Capturing the stars: predicting ratings for service and product reviews. In: Proceedings of the NAACL HLT Workshop on Semantic Search. Association for Computational Linguistics (2010)
Ganu, G., Elhadad, N., Marian, A.: Beyond the stars: improving rating predictions using review text content. In: Twelfth International Workshop on the Web and Database Providence, Rhode Island, USA (2009)
Qu, L., Ifrim, G., Weikum, G.: The bag-of-opinions method for review rating prediction from sparse text patterns. In: Proceedings of the 23rd International Conference on Computational Linguistics, Beijing, pp. 913–921 (2010)
Li, F., Liu, N., Jin, H., Zhao, K., Yang, Q., Zhu, X.: Incorporating reviewer and product information for review rating prediction. In: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence
Zheng, Z., Chin, T., Liu, N., Yang, Q., Yu, Y.: Rating prediction with informative ensemble of multi-resolution dynamic models. JMLR: Workshop and Conference Proceedings
Ye, Q., Zhang, Z.: Sentiment classification of online reviews to travel destinations by supervised machine learning approaches. Expert Syst. Appl. 6527–6535 (2009)
Moghaddam, S., Ester, M.: Opinion digger: an unsupervised opinion miner from unstructured product reviews. In: CIKM ‘10 Proceedings of the 19th ACM International Conference on Information and Knowledge Management, pp. 1825–1828
Zhou, H., Song, F.: Aspect-level sentiment analysis based on a generalized probabilistic topic and syntax model. In: Proceedings of the Twenty-Eighth International Florida Artificial Intelligence Research Society Conference (2015)
Kasper, W., Vela, M.: Sentiment analysis for hotel reviews. In: Proceedings of the Computational Linguistics-Applications Conference, Jacharanka, pp. 45–52 (2011)
Lu, B., Ott, M., Cardie, C., Tsou, B.: Multi-aspect sentiment analysis with topic models. In: IEEE 11th International Conference on Data Mining Workshop, pp. 81–88 (2011)
Juwattanasamran, P., Supattranuwong, S., Sinthupinyo, S.: Applying data mining to analyze travel pattern in searching travel destination choices. Int. J. Eng. Sci. 2 (2013)
Smola, A.J., Scholkopf, B.: A Tutorial on Support Vector Regression
Kavita, G., Zhai, C.: Opinion-based entity ranking. Inf. Retr. 116–150 (2012)
Shevade, S.K., Keerthi, S.S., Bhattacharyya, C., Murthy, K.R.K.: Improvements to the SMO algorithm for SVM regression. IEEE Trans. Neural Netw. 1188–1193 (1999)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Venugopalan, M., Nalayini, G., Radhakrishnan, G., Gupta, D. (2018). Rating Prediction Model for Reviews Using a Novel Weighted Textual Feature Method. In: Sa, P., Bakshi, S., Hatzilygeroudis, I., Sahoo, M. (eds) Recent Findings in Intelligent Computing Techniques . Advances in Intelligent Systems and Computing, vol 709. Springer, Singapore. https://doi.org/10.1007/978-981-10-8633-5_19
Download citation
DOI: https://doi.org/10.1007/978-981-10-8633-5_19
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-8632-8
Online ISBN: 978-981-10-8633-5
eBook Packages: EngineeringEngineering (R0)