Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Research

By Soujanya Poria, Devamanyu Hazarika, Navonil Majumder, Rada Mihalcea. IEEE Transactions on Affective Computing, 2020

308 citations recorded by the source corpus.

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 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.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/ab337fddaa65a608da16bfa402bae99c92fd34af

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Citations in the corpus

Multimodal sentiment analysis: A systematic review of history, datasets, multimodal fusion methods, applications, challenges and future directions (2022, background): “Bias can take many forms, including gender, colour, age, and so on [76].”
A survey on sentiment analysis methods, applications, and challenges (2022, background): “Mixed Code Data Code-mixing is the employment of vocabulary and grammar from different languages in same sentence (Pravalika et al. 2017; Poria et al. 2020).”
Learning Modality-Specific Representations with Self-Supervised Multi-Task Learning for Multimodal Sentiment Analysis (2021, background): “Multimodal Sentiment Analysis (MSA) attracts more and more attention in recent years (Zadeh et al. 2017; Tsai et al. 2019; Poria et al. 2020).”
Investigating Gender Bias in BERT (2020, background): “For instance, one of the MLP regressors predicts high emotion intensity scores to sentences with female words than male words under the same context (Poria et al. 2020).”
Multimodal Sentiment Analysis: A Survey of Methods, Trends, and Challenges (2023, background): “The major trends in sentiment analysis are lexical-based, machine learning-based, and deep learning-based approaches [12].”
Sentiment Analysis in the Era of Large Language Models: A Reality Check (2023, methodology): “Sentiment analysis 1 (SA) has been a long-established area of research in natural language processing (NLP), which aims to study people’s opinions, sentiments, emotions, etc, through computational methods (Liu, 2015; Poria et al., 2020).”