Social Media Trustable Reviews Based Sales Forecasting Using Polarity Parameters and Pattern Via Deep Learning Models
Keywords:
Natural Language Processing, Supervised Learning, Text Analytics, Computational Linguistics, Graph Neural Networks, Graph Convolutional NetworkAbstract
Nowadays, a lot of people use product reviews when making decisions. Nevertheless, reviewers manipulate the method by publishing fictitious reviews to elevate or denigrate the products because they do it for financial gain. Fake review detection has received a greater attention in the past decade from academic and industrial communities alike. Sentiment analysis recognizes and separates the opinion from the provided review; the analysis procedure involves text analytics, natural language processing (NLP), computational linguistics, and classifying the opinion's polarity. The lack of labeling materials for supervised learning and evaluation, however, means that the problem will continue to be difficult to solve. The problem has been approached from both the reviewer's and the reviewer's perspectives in numerous current works. For fake review prediction, we employed GloVe embedding along with BERT. The sentiment polarity for real reviews are then determined using Co-Sensitive Weighted Fusion GCN. It has a positive, negative, or neutral outcome. As a result, we are using the previous quadrants to forecast the current quarter sales possibility. Sales forecasting uses a variety of parameters, including subjectivity, tweet rate, polarity, mean, standard deviation, variance, skew, and kurtosis score of each polarity. Along with this parameter this approach introduces a temporal based polarity pattern to increase the efficiency of sales forecasting with the help of BiLSTM based regression model. The proposed method for fake news prediction HE-CNN_TB yields an F1_score of 90.85% for GossipCop dataset and 93.58% for the PolitiFact dataset. The suggested technique CoSWFGCN obtains an F1_score of 90% for the Amazon dataset and 76.19% for the Twitter_15 dataset in terms of sentiment polarity. The suggested BiLSTM-Pattern prediction model yields an average 0f 0.6 R2 value for the forecasting of the laptop brands such as Asus, HP, Dell and Lenovo.
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