Abstract
With the ever broad availability of technology, organizations and merchants are now able to collect large amounts of reviews online. Each review usually consists of an interval datum, such as a one to five ranking, along with a text comment. While an aggregated numeric scale commonly serves as an overall rating; a human user usually needs to read text manually to comprehend it. The aim of this work is to generate an explainable summary computationally through mining all text comments with natural language processing (NLP). In this initial work, we are able to derive an overall numeric scale of the reviews through sentiment analysis. We further combine methods of text summarization together with document clustering to obtain an explainable text summary that is easy for human users to comprehend. As an experiment, our approach has produced an explainable summary from a review dataset publicly available from Amazon.
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Notes
- 1.
Instead of Eq. (1), one may apply fuzzy memberships to categorize the sentiment of each \(d \in D\).
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Acknowledgment
This work is partially supported by the US National Science Foundation through the grant award NSF/OIA-1946391. The authors would also very much like to express their sincere appreciations to the contributors of high quality Python software tools. Applying such publicly available tools, we are able to significantly improve the efficiency and effectiveness of this work.
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Moody, A., Hu, C., Zhan, H., Spurling, M., Sheng, V.S. (2022). Towards Explainable Summary of Crowdsourced Reviews Through Text Mining. In: Ciucci, D., et al. Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2022. Communications in Computer and Information Science, vol 1601. Springer, Cham. https://doi.org/10.1007/978-3-031-08971-8_44
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DOI: https://doi.org/10.1007/978-3-031-08971-8_44
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