Traditional and Deep Learning Approaches for Sentiment Analysis: A Survey

Abstract

Presently, individuals generate tremendous volumes of information on the internet. As a result, sentiment analysis is a critical tool for automating a deep understanding of usergenerated information. Of late, deep learning algorithms have shown endless promises for a variety of sentiment analysis. The purpose of sentiment analysis is to categorize different descriptions as good, bad, or impartial based on context data. Numerous studies have been concentrated on sentiment analysis in addition to the ability to examine thoughts, views, and reactions. In this paper, we review classical and deep learning approaches that have been applied to various sentiment analysis tasks and their evolution over last years and provide performance analysis of different sentiment analysis models on particular datasets. In the end, we will highlight current challenges and suggested solutions that can be considered in future work to achieve better performance.

Text Classification and Cluster Analysis based on Deep Learning and Natural Language Processing

“…This method only repeats the cycle in B cycles and gets a model estimate of 1. CD method first uses training samples to initialize the visual layer and then uses the conditional probability method to find the hidden layer [14]. The visual layer is obtained from the hidden layer by the conditional distribution.…”