Deep learning-based multimodal emotion recognition from audio, visual, and text modalities: A systematic review of recent advancements and future prospects

S Zhang, Y Yang, C Chen, X Zhang, Q Leng… - Expert Systems with …, 2024‏ - Elsevier
Emotion recognition has recently attracted extensive interest due to its significant
applications to human–computer interaction. The expression of human emotion depends on …

[HTML][HTML] A survey on deep learning for textual emotion analysis in social networks

S Peng, L Cao, Y Zhou, Z Ouyang, A Yang, X Li… - Digital Communications …, 2022‏ - Elsevier
Abstract Textual Emotion Analysis (TEA) aims to extract and analyze user emotional states
in texts. Various Deep Learning (DL) methods have developed rapidly, and they have …

Beyond the imitation game: Quantifying and extrapolating the capabilities of language models

A Srivastava, A Rastogi, A Rao, AAM Shoeb… - arxiv preprint arxiv …, 2022‏ - arxiv.org
Language models demonstrate both quantitative improvement and new qualitative
capabilities with increasing scale. Despite their potentially transformative impact, these new …

An unexpectedly large count of trees in the West African Sahara and Sahel

M Brandt, CJ Tucker, A Kariryaa, K Rasmussen, C Abel… - Nature, 2020‏ - nature.com
A large proportion of dryland trees and shrubs (hereafter referred to collectively as trees)
grow in isolation, without canopy closure. These non-forest trees have a crucial role in …

[HTML][HTML] An emoji feature-incorporated multi-view deep learning for explainable sentiment classification of social media reviews

QA Xu, C Jayne, V Chang - Technological Forecasting and Social Change, 2024‏ - Elsevier
Sentiment analysis has demonstrated its value in a range of high-stakes domains. From
financial markets to supply chain management, logistics, and technology legitimacy …

Multi-label emotion classification in texts using transfer learning

I Ameer, N Bölücü, MHF Siddiqui, B Can… - Expert Systems with …, 2023‏ - Elsevier
Social media is a widespread platform that provides a massive amount of user-generated
content that can be mined to reveal the emotions of social media users. This has many …

Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm

B Felbo, A Mislove, A Søgaard, I Rahwan… - arxiv preprint arxiv …, 2017‏ - arxiv.org
NLP tasks are often limited by scarcity of manually annotated data. In social media sentiment
analysis and related tasks, researchers have therefore used binarized emoticons and …

A survey of state-of-the-art approaches for emotion recognition in text

N Alswaidan, MEB Menai - Knowledge and Information Systems, 2020‏ - Springer
Emotion recognition in text is an important natural language processing (NLP) task whose
solution can benefit several applications in different fields, including data mining, e-learning …

Procedural content generation via machine learning (PCGML)

A Summerville, S Snodgrass, M Guzdial… - IEEE Transactions …, 2018‏ - ieeexplore.ieee.org
This survey explores procedural content generation via machine learning (PCGML), defined
as the generation of game content using machine learning models trained on existing …

Stance detection with bidirectional conditional encoding

I Augenstein, T Rocktäschel, A Vlachos… - arxiv preprint arxiv …, 2016‏ - arxiv.org
Stance detection is the task of classifying the attitude expressed in a text towards a target
such as Hillary Clinton to be" positive", negative" or" neutral". Previous work has assumed …