Sentiment Analysis of Online Product Reviews Using DLMNN and Future Prediction of Online Product Using IANFIS

Abstract

Abstract A major task that the NLP (Natural Language Processing) has to follow is Sentiments analysis (SA) or opinions mining (OM). For finding whether the user's attitude is positive, neutral or negative, it captures each user's opinion, belief, and feelings about the corresponding product. Through this, needed changes can well be done on the product for better customer contentment by the companies. Most of the existent techniques on SA aimed at these online products have extremely low accuracy and also encompassed more time amid training. By employing a Deep learning modified neural network (DLMNN), a technique is proposed aimed at SA of online products review; in addition, via Improved Adaptive Neuro-Fuzzy Inferences System (IANFIS), a technique is proposed aimed at future prediction of online products to trounce the above-stated issues. Firstly, the data values are separated into Contents-based (CB), Grades-based (GB), along with Collaborations based (CLB) setting as of the dataset. Then, each setting goes via review analysis (RA) by employing DLMNN, which renders the results as negative, positive, in addition to neutral reviews. IANFIS carry out a weighting factor and classification on the product for upcoming prediction. In the experimental assessment, the proposed work gave an enhanced performance compared to the existing methods.