Product review opinion based on sentiment analysis

Sentiment analysis is a natural language processing (NLP) technique for determining emotional tone in a body of text. Using product reviews in sentiment analysis and opinion mining various methods have been developed previously. Although, existing product review analyzing techniques could not accurately detect the product aspect and non-aspect. Hence a novel Detach Frequency Assort is proposed to detect the product aspect term using TF-ISF (Term frequency-inverse sentence frequency) with Part of Speech (POS) tags for sentence segmentation and additionally using Feedback Neural Network to combine product aspect feedback loop. Furthermore, decision-making problem occurs during classification of sentiments. Hence, to solve this problem a novel technique named, Systemize Polarity Shift is proposed in which flow search based Support Vector Machine (SVM) with Bag of Words model classifies pre-trained review comments as positive, negative, and neutral sentiments. Moreover, the identification of specific products is not focused in sentiment analysis. Hence, a novel Revival Extraction is proposed in which a specific product is extracted based on thematic analysis method to obtain accurate data. Thus, the proposed Product Review Opinion framework gives effective optimized results in sentiment analysis with high accuracy, specificity, recall, sensitivity, F1-Score, and precision.

Automated summary

Sentiment analysis is a technique in NLP that determines emotional tone in text. Existing methods for analyzing product reviews have limitations in accurately detecting product aspects. This study proposes a Detach Frequency Assort method that combines TF-ISF with POS tags and Feedback Neural Network for product aspect detection. The study also introduces Systemize Polarity Shift, a flow search based SVM technique, to classify sentiments in review comments, and Revival Extraction, a thematic analysis method, to identify specific products. The proposed framework shows optimized results with high accuracy, specificity, recall, sensitivity, F1-Score, and precision in sentiment analysis.