Article
Computer Science, Information Systems
Weizhi Nie, Rihao Chang, Minjie Ren, Yuting Su, Anan Liu
Summary: Sentiment analysis and emotion detection in conversation have attracted increasing attention in the fields of social robot, social network, and intelligent voice assistant. In this paper, an incremental graph convolution network (I-GCN) is proposed to handle emotion detection in conversation. The graph structure is utilized to represent the semantic correlation information of utterances, while the incremental graph structure is used to preserve the temporal change information. Two types of GCN, namely utterance-level GCN (U-GCN) and speaker-level GCN (S-GCN), are introduced to learn the features of utterances. The parameters of the model are fine-tuned with new utterances to enhance the contribution of temporal change information. Experimental results show the effectiveness of the proposed method in three conversation corpuses.
IEEE TRANSACTIONS ON MULTIMEDIA
(2022)
Article
Computer Science, Artificial Intelligence
Anping Zhao, Yu Yu
Summary: The knowledge-enabled language representation model BERT proposed in this work enhances aspect-based sentiment analysis by injecting domain knowledge and leveraging an external sentiment knowledge graph, resulting in more accurate and explainable results.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Information Systems
Mohammed Al-Hashedi, Lay-Ki Soon, Hui-Ngo Goh, Amy Hui Lan Lim, Eu-Gene Siew
Summary: In order to address the negative impact of cyberbullying, extensive research has been conducted to propose effective solutions. This paper introduces cyberbullying detection models that utilize contextual, emotions, and sentiment features. An Emotion Detection Model (EDM) is developed using improved Twitter datasets with annotated emotions. Emotions and sentiment are extracted from cyberbullying datasets using the EDM and lexicons. The results reveal anger, fear, and guilt as major emotions associated with cyberbullying. The proposed models, incorporating emotion features and sentiment, outperform existing models in terms of recall by 0.5 to 0.6 and f1-score by 0.7. The contributions of this work include a comprehensive emotion-annotated dataset for cyberbullying detection and empirical evidence of emotions as effective features in cyberbullying detection.
Article
Computer Science, Information Systems
Naila Aslam, Furqan Rustam, Ernesto Lee, Patrick Bernard Washington, Imran Ashraf
Summary: Through sentiment analysis and emotion detection using tweets related to cryptocurrency, it was found that more people feel happy with the use of cryptocurrency, followed by fear and surprise. Machine learning models perform better when using BoW features. The proposed LSTM-GRU ensemble model outperforms other models in sentiment analysis and emotion prediction.
Article
Automation & Control Systems
Hande Aka Uymaz, Senem Kumova Metin
Summary: Detection of emotions from text has become an important research area in recent years. By adding emotional information to word vectors, researchers have achieved better results in emotion detection tasks, overcoming limitations of traditional word representation models.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Computer Science, Software Engineering
Zhenpeng Chen, Yanbin Cao, Huihan Yao, Xuan Lu, Xin Peng, Hong Mei, Xuanzhe Liu
Summary: Using emojis for sentiment and emotion detection can help address the issue of limited labeled data and has shown significant improvement in the field of software engineering.
ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY
(2021)
Article
Computer Science, Information Systems
Alejandro De Leon Langure, Mahdi Zareei
Summary: Sentiment analysis and text emotion detection are computer techniques used to analyze text and identify human emotions. However, most research focuses on comparing computational methods, while less attention is paid to selecting emotion models, training corpora, and data sources. The lack of standardization in these critical factors presents a challenge for meaningful performance comparisons among algorithms.
Article
Computer Science, Artificial Intelligence
Rui Mao, Qian Liu, Kai He, Wei Li, Erik Cambria
Summary: With the breakthrough of large-scale pre-trained language model (PLM) technology, prompt-based classification tasks, such as sentiment analysis and emotion detection, have gained increasing attention. This study conducts a systematic empirical study on prompt-based sentiment analysis and emotion detection to investigate the biases of PLMs in affective computing.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Rong Xiang, Jing Li, Mingyu Wan, Jinghang Gu, Qin Lu, Wenjie Li, Chu-Ren Huang
Summary: This study introduces a novel approach to incorporate external affective knowledge into neural networks for sentiment analysis, showing superior performance over traditional neural networks in all benchmark tests and with significant positive effects on model enhancement.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Automation & Control Systems
Jieyu An, Wan Mohd Nazmee Wan Zainon
Summary: Multimodal sentiment analysis is an important research area, especially in social media where emotions are expressed through text and images. This paper proposes a novel model called ICCI, which integrates color cues to improve sentiment analysis accuracy. The model extracts semantic and color features, and utilizes a cross-attention mechanism for feature interaction. Experimental results on benchmark datasets demonstrate the effectiveness of ICCI, outperforming existing methods with higher accuracy.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Somayyeh Behmanesh, Alireza Talebpour, Mehrnoush Shamsfard, Mohammad Mahdi Jafari
Summary: Recent AI studies have focused on developing question answering systems for automatic responses to natural language questions. Knowledge-based open domain question answering systems can accurately generate answers to questions in various fields. However, these systems require further development to scale answer retrieval and question interpretation. Deep learning methods are being used in this research area. Existing knowledge-based question answering systems use manually curated knowledge bases or knowledge bases automatically extracted from unstructured texts, or a combination of both. Limited access to knowledge bases in open domain question answering systems limits their expandability. Systems that use curated knowledge bases have high precision but limited coverage, while systems that use extracted knowledge bases have higher coverage but lower precision. To improve precision over extracted knowledge bases, a solution for enhancing relation span detection in questions is proposed in this paper. A dataset with 16,675 simple questions and answers based on Reverb triples is introduced. A method based on a fine-tuned BERT model is proposed for relation span detection in questions, resulting in a precision of 99.65%.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Chemistry, Analytical
Suhaib Kh. Hamed, Mohd Juzaiddin Ab Aziz, Mohd Ridzwan Yaakub
Summary: Nowadays, social media has become the main source of news worldwide, but the spread of fake news on such platforms has become a serious global issue with negative impacts on politics, economy, society, and people's lives. In this study, sentiment and emotion analysis were used to extract features from news articles and user comments, and these features were fed into a bidirectional long short-term memory model to detect fake news. The proposed model achieved a high detection accuracy of 96.77%, outperforming other state-of-the-art studies.
Article
Computer Science, Cybernetics
Alaa Alslaity, Rita Orji
Summary: Emotion detection and sentiment analysis techniques are important for understanding user emotions during interactive system use. The capability of machine learning to analyze big data for emotion extraction has led to increased research in this domain. This paper presents a systematic review of 123 papers on machine learning-based emotion detection, revealing trends in machine learning approaches, data sources, and evaluation metrics.
BEHAVIOUR & INFORMATION TECHNOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Zhaoxia Wang, Zhenda Hu, Seng-Beng Ho, Erik Cambria, Ah-Hwee Tan
Summary: This paper proposes a new explainable fine-grained multi-class sentiment analysis method called MiMuSA, which mimics human language understanding processes. It builds multiple knowledge bases to support sentiment understanding and can identify fine-grained multi-class sentiments. Experimental results show that MiMuSA outperforms other existing multi-class sentiment analysis methods in terms of accuracy and F1-Score.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Farhat Ullah, Xin Chen, Syed Bilal Hussain Shah, Saoucene Mahfoudh, Muhammad Abul Hassan, Nagham Saeed
Summary: Emotion detection and sentiment analysis are important in identifying individuals' interest levels. While English and Chinese have received much attention in the field, the research on poor-resource languages like Urdu has been lacking. This study focuses on Roman Urdu and proposes a CNN-LSTM method with Word2Vec for emotion detection and sentiment analysis, which outperforms other approaches. The accuracy of emotion detection increased from 85% to 95%, and sentiment analysis improved from 89% to 93.3%.
Editorial Material
Computer Science, Information Systems
Alexandra Balahur, Guillaume Jacquet
INFORMATION PROCESSING & MANAGEMENT
(2015)
Article
Computer Science, Information Systems
Alexandra Balahur, Jose M. Perea-Ortega
INFORMATION PROCESSING & MANAGEMENT
(2015)
Article
Computer Science, Artificial Intelligence
Yoan Gutierrez, Sonia Vazquez, Andres Montoyo
EXPERT SYSTEMS WITH APPLICATIONS
(2016)
Article
Computer Science, Artificial Intelligence
Yoan Gutierrez, Sonia Vazquez, Andres Montoyo
KNOWLEDGE-BASED SYSTEMS
(2017)
Editorial Material
Computer Science, Artificial Intelligence
Alexandra Balahur, Rada Mihalcea, Andres Montoyo
COMPUTER SPEECH AND LANGUAGE
(2014)
Article
Computer Science, Artificial Intelligence
Alexandra Balahur, Marco Turchi
COMPUTER SPEECH AND LANGUAGE
(2014)
Article
Computer Science, Artificial Intelligence
Alexandra Balahur, Jesus M. Hermida, Andres Montoyo, Rafael Munoz
DATA & KNOWLEDGE ENGINEERING
(2013)
Article
Computer Science, Artificial Intelligence
Andres Montoyo, Patricio Martinez-Barco, Alexandra Balahur
DECISION SUPPORT SYSTEMS
(2012)
Article
Computer Science, Information Systems
Jesus M. Hermida, Santiago Melia, Andres Montoyo, Jaime Gomez
INFORMATION SYSTEMS FRONTIERS
(2013)
Article
Mathematics, Applied
Miguel Lloret-Climent, Josue-Antonio Nescolarde-Selva, Kristian Alonso-Stenberg, Andres Montoyo, Yoan Gutierrez-Vazquez
Summary: This study analyzed the life cycle and competitiveness of Benidorm's tourism system using tourist accommodation supply and demand statistics, and determined that the tourist system of Benidorm is currently in the rejuvenation phase with the help of Smart software's network analysis.
MATHEMATICAL METHODS IN THE APPLIED SCIENCES
(2022)
Proceedings Paper
Linguistics
Alexandra Balahur, Marco Turchi, Ralf Steinberger, Jose-Manuel Perea-Ortega, Guillaume Jacquet, Dilek Kuecuek, Vanni Zavarella, Adil El Ghali
LREC 2014 - NINTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION
(2014)
Proceedings Paper
Computer Science, Information Systems
Neili Machado, Carlos Balmaseda, Andres Montoyo
PROCEEDINGS OF THE 2014 9TH IBERIAN CONFERENCE ON INFORMATION SYSTEMS AND TECHNOLOGIES (CISTI 2014)
(2014)
Article
Management
Miguel A. Guerrero, Yolanda Villacampa, Andres Montoyo
INTERNATIONAL JOURNAL OF PROJECT MANAGEMENT
(2014)
