Opinion mining using Double Channel CNN for Recommender System
Minoo Sayyadpour Ali Nazarizadeh
Much unstructured data has been produced with the growth of the Internet and
social media. A significant volume of textual data includes users' opinions
about products in online stores and social media. By exploring and categorizing
them, helpful information can be acquired, including customer satisfaction,
user feedback about a particular event, predicting the sale of a specific
product, and other similar cases. In this paper, we present an approach for
sentiment analysis with a deep learning model and use it to recommend products.
A two-channel convolutional neural network model has been used for opinion
mining, which has five layers and extracts essential features from the data. We
increased the number of comments by applying the SMOTE algorithm to the initial
dataset and balanced the data. Then we proceed to cluster the aspects. We also
assign a weight to each cluster using tensor decomposition algorithms that
improve the recommender system's performance. Our proposed method has reached
91.6% accuracy, significantly improved compared to previous aspect-based
approaches.