E-commerce review sentiment score prediction considering misspelled words: a deep learning approach

Sakshi Jain () and Pradeep Kumar Roy ()
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Sakshi Jain: Vellore Institute of Technology
Pradeep Kumar Roy: Indian Institute of Information Technology

Electronic Commerce Research, 2024, vol. 24, issue 3, No 10, 1737-1761

Abstract: Abstract Acquiring a single sentiment score dependent on all the reviews will benefit the buyers and sellers in making the decision more accurately. The raw format of user-generated content lacks a legitimate language structure. It, therefore, acts as an obstacle for applying the Sentiment analysis task, which aims to predict the true emotion of a sentence by providing a score and its nature. This paper concentrates on obtaining a single sentiment score using a hybrid Long Short-Term Memory encoder–decoder model. This research uses the text normalization process to transform the sentences consisting of noise, appearing as incorrect grammar, abbreviations, freestyle, and typographical errors, into their canonical structure. The experimental outcomes confirm that the proposed hybrid model performs well in standardizing the raw E-commerce website review, enriched with hidden information and provided a single sentiment score influenced by all the review scores for the product.

Keywords: Sentiment analysis; Product review; Review correction; LSTM; Deep learning (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10660-022-09582-4

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