A framework for automated rating of online reviews against the underlying topics

Dai, Xiangfeng, Spasic, Irena ORCID: https://orcid.org/0000-0002-8132-3885 and Andres, Frederic 2017. A framework for automated rating of online reviews against the underlying topics. Presented at: ACM Southeast Conference, Kennesaw State University, Georgia, USA, 13-15 April 2017. ACM SE '17 Proceedings of the SouthEast Conference. New York: ACM, pp. 164-167. 10.1145/3077286.3077291

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Abstract

Even though the most online review systems offer star rating in addition to free text reviews, this only applies to the overall review. However, different users may have different preferences in relation to different aspects of a product or a service and may struggle to extract relevant information from a massive amount of consumer reviews available online. In this paper, we present a framework for extracting prevalent topics from online reviews and automatically rating them on a 5-star scale. It consists of five modules, including linguistic pre-processing, topic modelling, text classification, sentiment analysis, and rating. Topic modelling is used to extract prevalent topics, which are then used to classify individual sentences against these topics. A state-of-the-art word embedding method is used to measure the sentiment of each sentence. The two types of information associated with each sentence – its topic and sentiment – are combined to aggregate the sentiment associated with each topic. The overall topic sentiment is then projected onto the 5-star rating scale. We use a dataset of Airbnb online reviews to demonstrate a proof of concept. The proposed framework is simple and fully unsupervised. It is also domain independent, and, therefore, applicable to any other domains of products and services.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Additional Information:	© {Dai/Spasic/Andres| ACM} {2017}. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM SE '17 Proceedings of the SouthEast Conference, http://dx.doi.org/10.1145/3077286.3077291."
Publisher:	ACM
ISBN:	978-1-4503-5024-2
Date of First Compliant Deposit:	26 May 2017
Last Modified:	21 Oct 2022 07:26
URI:	https://orca.cardiff.ac.uk/id/eprint/99855

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