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

Author

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  • Sakshi Jain

    (Vellore Institute of Technology)

  • Pradeep Kumar Roy

    (Indian Institute of Information Technology)

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.

Suggested Citation

  • Sakshi Jain & Pradeep Kumar Roy, 2024. "E-commerce review sentiment score prediction considering misspelled words: a deep learning approach," Electronic Commerce Research, Springer, vol. 24(3), pages 1737-1761, September.
    • Handle: RePEc:spr:elcore:v:24:y:2024:i:3:d:10.1007_s10660-022-09582-4
    DOI: 10.1007/s10660-022-09582-4
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    References listed on IDEAS

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    1. Singh, Jyoti Prakash & Irani, Seda & Rana, Nripendra P. & Dwivedi, Yogesh K. & Saumya, Sunil & Kumar Roy, Pradeep, 2017. "Predicting the “helpfulness” of online consumer reviews," Journal of Business Research, Elsevier, vol. 70(C), pages 346-355.
    2. Yani Wang & Jun Wang & Tang Yao, 2019. "What makes a helpful online review? A meta-analysis of review characteristics," Electronic Commerce Research, Springer, vol. 19(2), pages 257-284, June.
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