Emotion recognition in conversation: Research challenges, datasets, and recent advances
Emotion is intrinsic to humans and consequently, emotion understanding is a key part of
human-like artificial intelligence (AI). Emotion recognition in conversation (ERC) is …
human-like artificial intelligence (AI). Emotion recognition in conversation (ERC) is …
[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 …
in texts. Various Deep Learning (DL) methods have developed rapidly, and they have …
A survey of textual emotion recognition and its challenges
Textual language is the most natural carrier of human emotion. In natural language
processing, textual emotion recognition (TER) has become an important topic due to its …
processing, textual emotion recognition (TER) has become an important topic due to its …
[PDF][PDF] BERT-CNN: A Deep Learning Model for Detecting Emotions from Text.
Due to the widespread usage of social media in our recent daily lifestyles, sentiment
analysis becomes an important field in pattern recognition and Natural Language …
analysis becomes an important field in pattern recognition and Natural Language …
Chinese sentiment classification based on Word2vec and vector arithmetic in human–robot conversation
K Jia - Computers and Electrical Engineering, 2021 - Elsevier
With the aim of enhancing human–chatbot interactions, this paper presents a novel
sentiment classification framework that recognizes both semantics emotion terms and …
sentiment classification framework that recognizes both semantics emotion terms and …
[HTML][HTML] An adaptive cost-sensitive learning approach in neural networks to minimize local training–test class distributions mismatch
We design an adaptive learning algorithm for binary classification problems whose objective
is to reduce the cost of misclassified instances derived from the consequences of errors. Our …
is to reduce the cost of misclassified instances derived from the consequences of errors. Our …
Contextual emotion detection using ensemble deep learning
A Thiab, L Alawneh, ALS Mohammad - Computer Speech & Language, 2024 - Elsevier
Emotion detection from online textual information is gaining more attention due to its
usefulness in understanding users' behaviors and their desires. This is driven by the large …
usefulness in understanding users' behaviors and their desires. This is driven by the large …
Contextual information usage for the enhancement of basic emotion classification in a weakly labelled social network dataset in Spanish
Basic emotion classification is one of the main tasks of Sentiment Analysis usually
performed by using several machine learning techniques. One of the main issues in …
performed by using several machine learning techniques. One of the main issues in …
A mutli-task mutlimodal framework for tweet classification based on cnn (grand challenge)
S Bansal - 2020 IEEE sixth international conference on …, 2020 - ieeexplore.ieee.org
The paper describes the system description and design for our solution submitted for the
MeToo BigMM Grand Challenge (BMGC). The challenge involves building a multimodal …
MeToo BigMM Grand Challenge (BMGC). The challenge involves building a multimodal …
Attention-based approaches for text analytics in social media and automatic summarization
JÁ González Barba - 2021 - riunet.upv.es
[EN] Nowadays, society has access, and the possibility to contribute, to large amounts of the
content present on the internet, such as social networks, online newspapers, forums, blogs …
content present on the internet, such as social networks, online newspapers, forums, blogs …