Research Area:  Machine Learning

Abstract:

Sentiment analysis, one of the research hotspots in the natural language processing field, has attracted the attention of researchers, and research papers on the field are increasingly published. Many literature reviews on sentiment analysis involving techniques, methods, and applications have been produced using different survey methodologies and tools, but there has not been a survey dedicated to the evolution of research methods and topics of sentiment analysis. There have also been few survey works leveraging keyword co-occurrence on sentiment analysis. Therefore, this study presents a survey of sentiment analysis focusing on the evolution of research methods and topics. It incorporates keyword co-occurrence analysis with a community detection algorithm. This survey not only compares and analyzes the connections between research methods and topics over the past two decades but also uncovers the hotspots and trends over time, thus providing guidance for researchers. Furthermore, this paper presents broad practical insights into the methods and topics of sentiment analysis, while also identifying technical directions, limitations, and future work.

Keywords:  
Sentiment analysis
Keyword co-occurrence analysis
Evolution analysis
Machine Learning
Deep Learning

Author(s) Name:  Jingfeng Cui, Zhaoxia Wang, Seng-Beng Ho & Erik Cambria

Publisher name:  Springer

DOI:  10.1007/s10462-022-10386-z

Volume Information:  

Paper Link:   https://link.springer.com/article/10.1007/s10462-022-10386-z