A Framework for Automated Rating of Online Reviews Against the Underlying Topics

What is it about?

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.

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Photo by Towfiqu barbhuiya on Unsplash

Photo by Towfiqu barbhuiya on Unsplash

Why is it important?

We presented a framework for rating online reviews against automatically extracted underlying topics. The proposed framework consists of modules: (1) linguistic preprocessing, (2) topic modeling, (3) sentence classification against the topics extracted in the previous module, (4) sentiment analysis, (5) rating against the topics based on the sentiment of the corresponding sentences. The proposed method is unsupervised, i.e. does not require an annotated training dataset. It is also domain independent, and, therefore, can be applied across different domains for which online reviews are available.