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

• URL: http://arxiv.org/abs/2005.00357v5
• Date: Mon, 16 Nov 2020 15:21:22 GMT
• Title: Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Research
• Authors: Soujanya Poria, Devamanyu Hazarika, Navonil Majumder, Rada Mihalcea
• Abstract summary: Sentiment analysis has come a long way since it was first introduced as a task nearly 20 years ago. There is an underlying perception that this field has reached its maturity. 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.
• Score: 49.32039466553038
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

Related papers

• A Comprehensive Review on Sentiment Analysis: Tasks, Approaches and Applications [0.2717221198324361]
  Sentiment analysis (SA) is an emerging field in text mining. It is the process of computationally identifying and categorizing opinions expressed in a piece of text over different social media platforms.
  arXiv  Detail & Related papers  (2023-11-19T06:29:41Z)
• Visual Crowd Analysis: Open Research Problems [2.462045767312954]
  Modern deep-learning approaches have made it possible to develop fully-automated vision-based crowd-monitoring applications. Despite the magnitude of the issue at hand, the significant technological advancements, and the consistent interest of the research community, there are still numerous challenges that need to be overcome.
  arXiv  Detail & Related papers  (2023-08-21T12:24:20Z)
• A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning [58.107474025048866]
  Forgetting refers to the loss or deterioration of previously acquired knowledge. Forgetting is a prevalent phenomenon observed in various other research domains within deep learning.
  arXiv  Detail & Related papers  (2023-07-16T16:27:58Z)
• Multimodal Sentiment Analysis: A Survey [5.910558593808665]
  This review provides an overview of the definition, background, and development of multimodal sentiment analysis. It also covers recent datasets and advanced models, emphasizing the challenges and future prospects of this technology.
  arXiv  Detail & Related papers  (2023-05-12T16:56:13Z)
• Fairness in Recommender Systems: Research Landscape and Future Directions [119.67643184567623]
  We review the concepts and notions of fairness that were put forward in the area in the recent past. We present an overview of how research in this field is currently operationalized. Overall, our analysis of recent works points to certain research gaps.
  arXiv  Detail & Related papers  (2022-05-23T08:34:25Z)
• Fine-Grained Image Analysis with Deep Learning: A Survey [146.22351342315233]
  Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer vision and pattern recognition. This paper attempts to re-define and broaden the field of FGIA by consolidating two fundamental fine-grained research areas -- fine-grained image recognition and fine-grained image retrieval.
  arXiv  Detail & Related papers  (2021-11-11T09:43:56Z)
• Unsupervised Domain Adaptation in Semantic Segmentation: a Review [22.366638308792734]
  The aim of this paper is to give an overview of the recent advancements in the Unsupervised Domain Adaptation (UDA) of deep networks for semantic segmentation.
  arXiv  Detail & Related papers  (2020-05-21T20:10:38Z)
• Fine-grained Financial Opinion Mining: A Survey and Research Agenda [50.27357144360525]
  We first define the financial opinions from both coarse-grained and fine-grained points of views, and then provide an overview on the issues already tackled. We propose a road map of fine-grained financial opinion mining for future researches, and point out several challenges yet to explore.
  arXiv  Detail & Related papers  (2020-05-05T01:07:07Z)
• Survey on Visual Sentiment Analysis [87.20223213370004]
  This paper reviews pertinent publications and tries to present an exhaustive overview of the field of Visual Sentiment Analysis. The paper also describes principles of design of general Visual Sentiment Analysis systems from three main points of view. A formalization of the problem is discussed, considering different levels of granularity, as well as the components that can affect the sentiment toward an image in different ways.
  arXiv  Detail & Related papers  (2020-04-24T10:15:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.