2020/05/01 by Soujanya Poria, Devamanyu Hazarika, Poria, Soujanya +5 · 1 voice · 10 citations
Computer Science · Social Sciences · #Artificial intelligence #Chart #Computational and Text Analysis Methods #Computer science #Data science #Economics #Epistemology #Field (mathematics) #Financial economics #LEAPS #Maturity (psychological) #Perception #Polarity (international relations) #Political science #Relevance (law) #Sentiment Analysis and Opinion Mining #Sentiment analysis #Topic Modeling #cs.CL #cs.IR
paper · pdf · doi:10.48550/arxiv.2005.00357
published in arXiv (Cornell University) (Cornell University) · Published in the IEEE Transactions on Affective Computing (TAFFC)
openalex publication_date 2020/05/01 · arxiv created 2020/11/16 · arxiv updated 2020/11/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Sentiment analysis as a field has come a long way since it was first introduced as a task nearly 20 years ago. It has widespread commercial applications in various domains like marketing, risk management, market research, and politics, to name a few. Given its saturation in specific subtasks -- such as sentiment polarity classification -- and datasets, there is an underlying perception that this field has reached its maturity. In this article, we discuss this perception by pointing out the shortcomings and under-explored, yet key aspects of this field that are necessary to attain true sentiment understanding. We analyze the significant leaps responsible for its current relevance. Further, we attempt to chart a possible course for this field that covers many overlooked and unanswered questions.