Article
Computer Science, Artificial Intelligence
Srishti Vashishtha, Seba Susan
Summary: This paper introduces an unsupervised sentiment classification system that computes sentiment scores and polarity of phrases using the SentiWordNet lexicon and fuzzy linguistic hedges, extracting significant keyphrases for sentiment analysis. Experimental results demonstrate high accuracy compared to other state-of-the-art methods.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Automation & Control Systems
Debajyoty Banik
Summary: This paper explores how to preserve sentiment in machine translation and improve translation accuracy by introducing a sentiment model.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Engineering, Electrical & Electronic
Siyu Li, Kui Zhao, Jin Yang, Xinyun Jiang, Zhengji Li, Zicheng Ma
Summary: This paper proposes the Senti-eXLM model, which improves the understanding and representation capability of Uyghur language for text emotion analysis through adaptive knowledge domain expansion and dynamic model adjustment.
ELECTRONICS LETTERS
(2022)
Article
Computer Science, Information Systems
Housheng Xie, Wei Lin, Shuying Lin, Jin Wang, Liang-Chih Yu
Summary: Dimensional sentiment analysis focuses on representing affective states as continuous numerical values on multiple dimensions, taking into account the relationships between different dimensions for better prediction accuracy. The proposed multi-dimensional relation model incorporates these relationships into deep neural networks, outperforming traditional models that treat each dimension independently. Internal mode, which integrates dimension relations before prediction, showed better performance than the external mode, and a combination of both modes achieved the best results.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Ibtissam Touahri, Azzeddine Mazroui
Summary: This article discusses how to improve the accuracy of sentiment analysis system by utilizing sarcastic features, which is challenging due to the implicit nature of sarcasm and incongruity in context. By extracting features, building sentimental, offensive, and sarcastic lexicons, as well as collecting corpora, enhancements were made to the sentiment analysis system.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Information Systems
Xuemin Yu, Martha Dais Ferreira, Fernando V. Paulovich
Summary: With governments taking measures against COVID-19, public sentiment fluctuates and social media becomes the main platform for expressing emotions and opinions. Senti-COVID19 utilizes sentiment analysis to assist governments in formulating measures and policies, providing detailed information and visualizations.
Article
Education & Educational Research
Chen Hsueh Chu, Tian Jing Xuan
Summary: This study aimed to develop a corpus-aided pronunciation teacher-training programme and examine its effectiveness in English classrooms in Hong Kong. The results showed that the training provided sufficient knowledge about corpus-aided pronunciation teaching and task design. The findings demonstrated the willingness of students to use corpus data to raise awareness of common pronunciation errors and suggested the effectiveness of the flipped classroom approach.
INTERACTIVE LEARNING ENVIRONMENTS
(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, Artificial Intelligence
Slavko Zitnik, Neli Blagus, Marko Bajec
Summary: The rapid growth of social media, news sites, and blogs has led to an increase in expressing and sharing opinions on the internet. Opinion mining or sentiment analysis has become an important research discipline in the past decade. This paper focuses on target-level sentiment analysis, where the task is to predict the sentiment towards specific entities mentioned throughout the document. The study presents a new annotated dataset of Slovene news articles and compares the task with traditional sentiment analysis using various machine learning and deep neural network approaches. The results demonstrate the effectiveness of a customized BERT adapter in achieving the best results.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Xi Wang, Mengmeng Fan, Mingming Kong, Zheng Pei
Summary: In this paper, a self-supervised attention learning approach, which enhances sentiment lexical strength, is proposed to improve sentiment analysis performance. The experiments on three datasets demonstrate that the approach can effectively improve the performance and provide explanations for text classification.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Chemistry, Multidisciplinary
Qizhi Li, Xianyong Li, Yajun Du, Yongquan Fan, Xiaoliang Chen
Summary: This paper proposes a new sentiment-enhanced word embedding method to improve sentence-level sentiment classification. By leveraging the mapping relationship between word embeddings and sentiment orientations, the method achieves higher accuracy and F1 values and reduces convergence time in sentiment classification models.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Artificial Intelligence
Kia Dashtipour, Mandar Gogate, Erik Cambria, Amir Hussain
Summary: Recent studies show that utilizing multimodal data can effectively gauge public perception, and a Persian multimodal dataset and sentiment analysis framework are provided as research resources. Experimental results demonstrate that integrating multimodal features can enhance sentiment analysis performance.
Article
Computer Science, Artificial Intelligence
Zijian Feng, Hanzhang Zhou, Zixiao Zhu, Kezhi Mao
Summary: TTA is a novel data augmentation technique for sentiment analysis that improves the model's generalization capability through probabilistic word sampling for synonym replacement and application of zero masking or contextual replacement to discriminative words irrelevant to sentiment.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Jie Chen, Nan Song, Yansen Su, Shu Zhao, Yanping Zhang
Summary: This study designed a novel approach to analyze the sentiment orientation of users in social networks, improving sentiment analysis performance by integrating user interactions and opinion data.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Kitsuchart Pasupa, Thititorn Seneewong Na Ayutthaya
Summary: The paper presents a framework for sentiment analysis in Thai and utilizes different types of features, improving overall performance by combining deep learning algorithms, with the best model being BLSTM-CNN.
COGNITIVE COMPUTATION
(2022)
Editorial Material
Computer Science, Information Systems
Paolo Rosso, Cristina Bosco, Rossana Damiano, Viviana Patti, Erik Cambria
INFORMATION PROCESSING & MANAGEMENT
(2016)
Article
Computer Science, Artificial Intelligence
Mirko Lai, Alessandra Teresa Cignarella, Delia Irazu Hernandez Farias, Cristina Bosco, Viviana Patti, Paolo Rosso
COMPUTER SPEECH AND LANGUAGE
(2020)
Review
Computer Science, Interdisciplinary Applications
Fabio Poletto, Valerio Basile, Manuela Sanguinetti, Cristina Bosco, Viviana Patti
Summary: Hate speech detection in social media has recently gained significant traction in the Natural Language Processing community, with annotated corpora and benchmarks being key resources. Lexica also play an important role in the development of hate speech detection systems. The analysis highlights a heterogeneous, growing landscape with several issues and venues for improvement.
LANGUAGE RESOURCES AND EVALUATION
(2021)
Article
Computer Science, Information Systems
Marco Polignano, Valerio Basile, Pierpaolo Basile, Giuliano Gabrieli, Marco Vassallo, Cristina Bosco
Summary: This paper proposes a hybrid model BERT-WMAL that combines information from a recent transformer deep learning model and a polarized lexicon. The model achieves comparable performances to the state-of-the-art models in sentence polarity while providing an explanation of the important terms involved in the prediction. The model shows improved F1 scores on multiple datasets, and a user study validates the effectiveness of the approach.
INFORMATION PROCESSING & MANAGEMENT
(2022)
Article
Computer Science, Information Systems
Cristina Bosco, Viviana Patti, Simona Frenda, Alessandra Teresa Cignarella, Marinella Paciello, Francesca D'Errico
Summary: This paper studies the formation and application of stereotypes from the perspectives of psychology and computational linguistics. By building an Italian social media corpus and conducting lexical analysis and experiments, the aim is to provide theoretical basis for the development of tools that automatically detect and label Italian stereotypes.
INFORMATION PROCESSING & MANAGEMENT
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Mirko Lai, Marco Antonio Stranisci, Cristina Bosco, Rossana Damiano, Viviana Patti
Summary: The paper presents a method for inferring user attitudes from text in their messages and ranks hate speech spreaders on Twitter based on accuracy. It analyzes false negative and false positive predictions and shows that fine-tuning features based on user morality and named entities can aid in detecting hate speech spreaders.
EXPERIMENTAL IR MEETS MULTILINGUALITY, MULTIMODALITY, AND INTERACTION (CLEF 2022)
(2022)
Proceedings Paper
Computer Science, Information Systems
Farah Benamara, Cristina Bosco, Viviana Patti, Elisabetta Fersini, Gabriella Pasi, Marco Viviani
2018 IEEE 5TH INTERNATIONAL CONFERENCE ON DATA SCIENCE AND ADVANCED ANALYTICS (DSAA)
(2018)
Proceedings Paper
Computer Science, Information Systems
Arthur T. E. Capozzi, Viviana Patti, Giancarlo Ruffo, Cristina Bosco
WS.2 2018: PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON WEB STUDIES
(2018)
Proceedings Paper
Computer Science, Artificial Intelligence
Delia Irazu Hernandez Farias, Cristina Bosco, Viviana Patti, Paolo Rosso
COMPUTATIONAL LINGUISTICS AND INTELLIGENT TEXT PROCESSING, CICLING 2017, PT II
(2018)
Proceedings Paper
Computer Science, Artificial Intelligence
Jihen Karoui, Farah Benamara, Veronique Moriceau, Viviana Patti, Cristina Bosco, Nathalie Aussenac-Gilles
15TH CONFERENCE OF THE EUROPEAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (EACL 2017), VOL 1: LONG PAPERS
(2017)
Proceedings Paper
Computer Science, Information Systems
Mirko Lai, Cristina Bosco, Viviana Patti, Daniela Virone
PROCEEDINGS OF THE 2015 IEEE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND ADVANCED ANALYTICS (IEEE DSAA 2015)
(2015)
Proceedings Paper
Computer Science, Artificial Intelligence
Cristina Bosco, Viviana Patti, Andrea Bolioli
PROCEEDINGS OF THE TWENTY-FOURTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI)
(2015)
Proceedings Paper
Linguistics
Manuela Sanguinetti, Cristina Bosco, Loredana Cupi
LREC 2014 - NINTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION
(2014)
Proceedings Paper
Linguistics
Anita Alicante, Cristina Bosco, Anna Corazza, Alberto Lavelli
LREC 2012 - EIGHTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION
(2012)
