A Productive Review on Sentimental Analysis for High Classification Rates

Jaitly, Gaurika; Kapil, Manoj

doi:10.1007/978-981-15-6353-9_25

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…In general, opinion spamming is classified under various categories. 1 Such types of reviews may either be optimistic or pessimistic. The optimistic comments for a particular product grab center of attraction than the pessimistic comments.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy artificial bee colony‐based CNN‐LSTM and semantic feature for fake product review classification

Jacob¹,

Rajendran²

2021

Concurrency and Computation

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In recent years, online reviews are considered as the most significant resource for consumers to make a decision regarding the purchase of a particular product. The reviews can either encourage or relegate a product; therefore posting fake reviews has turned into a money-spinning business in the modern period. The detection of fake reviews has become a center of attraction for various business people. This research study aims in detecting fake product reviews using four significant phases namely the data pre-processing, feature extraction, feature selection, and classification. The features obtained in the pre-processing phase are extracted and selected using chi-squared technique to obtain a delegate subset among all data and to reduce the complication issues. Then a CNNLSTM-FABC approach classifies and detects the review as fake or real. Finally, the performance evaluation and the comparative analysis are carried out to determine the effectiveness of the proposed approach. The results reveal that the proposed approach performs well irrespective of the product type and sentiment polarity.

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Section: Introductionmentioning

confidence: 99%