Abstract
Online reviews are very important in the customer’s decision-making process in selecting the appropriate products in the online shopping portal. These reviews are then analyzed by business organizations to understand customer sentiment w.r.t. product/service. Traditional sentiment analysis techniques identify only positive, negative or neutral sentiment w.r.t. reviews and does not consider informativeness of reviews while analyzing sentiment. The extreme opinions like Praise and complaint sentences are considered as a subset of positive and negative sentences and becomes difficult to find. Praise sentences are more descriptive in nature. Praises contain more nouns, adjectives, intensifiers as compared to plain positive sentences and complaint sentences contain more connectives and adverbs rather than the plain negative sentences. This paper proposes a Linguistic feature-based approach for review sentences filtering and Hybrid feature selection method for classifying review sentence as Praise or Complaint.
These Praise and Complaint sentences can be further analyzed by business organizations to identify the reasons for customer satisfaction or dissatisfaction. It can also be used for creating automatic product description from online reviews in terms of pro and con of the product/service. The performance of the four different supervised Machine Learning classifiers, namely Random forest, SVC, KNeighbors, MLP with hybrid feature selection method is evaluated on three domains reviews using the parameters Accuracy, Precision, Recall, and F1-score. The proposed method showed excellent results as compared to the state of art classifiers.
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Khedkar, S., Shinde, S. (2020). Linguistic Feature-Based Praise or Complaint Classification from Customer Reviews. In: Pandian, A., Ntalianis, K., Palanisamy, R. (eds) Intelligent Computing, Information and Control Systems. ICICCS 2019. Advances in Intelligent Systems and Computing, vol 1039. Springer, Cham. https://doi.org/10.1007/978-3-030-30465-2_52
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DOI: https://doi.org/10.1007/978-3-030-30465-2_52
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