Article
Computer Science, Artificial Intelligence
Marouane Birjali, Mohammed Kasri, Abderrahim Beni-Hssane
Summary: Sentiment analysis, also known as Opinion Mining, is the task of extracting and analyzing people's opinions and emotions towards different entities. It is a powerful tool used by businesses, governments, and researchers to gain insights and make better decisions. This paper provides a comprehensive study of sentiment analysis methods, challenges, and trends for researchers in the field.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Xin Ye, Hongxia Dai, Lu-an Dong, Xinyue Wang
Summary: The study proposes a novel multi-view ensemble learning method to better integrate information from different features for improved microblog sentiment classification. Through two stages of processing, local fusion and global fusion, basic classifiers are combined into multiple classifier groups for classification, with experimental results showing that this method outperforms other methods in identifying the polarities of microblog posts.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Wonchan Choi, Besiki Stvilia, Hyun Seung Lee
Summary: Social Q&A sites have the potential to be a valuable source of online information, but little attention has been given to what influences users' credibility assessments of information in this context. This study developed a framework for web credibility assessment specific to social Q&A platforms, based on a literature analysis and case study of Stack Exchange and Wikipedia Reference Desk. The findings showed that content-related attributes were most frequently identified as cues for credibility assessments, followed by author-related and design-related factors, which had been rarely included in previous models of web credibility.
INFORMATION PROCESSING & MANAGEMENT
(2023)
Article
Physics, Multidisciplinary
Yang Zhang, Ji-Qing Lian, Ren-De Li, Hong-Tao Duan
Summary: This paper presents a method for studying the evolutionary characteristics of netizens' comment focus in university online public opinion. The proposed framework effectively analyzes comment data and provides valuable insights for managing public opinion.
FRONTIERS IN PHYSICS
(2023)
Editorial Material
Computer Science, Theory & Methods
David Camacho, Ma Victoria Luzon, Erik Cambria
Summary: The rapid growth of social media platforms and their associated applications has revolutionized the way billions of people interact on the Web, driving innovations in user-centered applications and efficient data processing techniques. Recent advances in data science and artificial intelligence have made these developments possible, as evidenced by the 12 selected papers in this special issue representing the latest advances in fields like pattern recognition, information fusion, knowledge discovery, and data visualization.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2021)
Article
Computer Science, Information Systems
Hongzhi Zhu, Fang Wang
Summary: This study explores the mechanism of how the emotion of social media users impacts market value and provides new understandings of signal transmission. The authors confirm the correlations between government microblog emotion, comment emotion, and market value by analyzing observation data on Chinese scenic spots. The study also verifies the moderating roles of the COVID-19 epidemic and tourism attention, as well as the crowding-out effect of tourism attention on comment emotion.
INFORMATION PROCESSING & MANAGEMENT
(2022)
Review
Computer Science, Information Systems
Miguel A. Alonso, David Vilares, Carlos Gomez-Rodriguez, Jesus Vilares
Summary: Fake news has been on the rise in recent years, posing a serious threat to social cohesion and trust in leaders. Automatic systems for fake news detection have become increasingly important due to the unfeasibility of manual verification, with sentiment analysis playing a key role in this process.
Article
Computer Science, Artificial Intelligence
Zou Xiaomei, Yang Jing, Zhang Wei, Han Hongyu
Summary: This study proposes a collaborative microblog sentiment analysis approach based on personalized sentiment analysis methods and utilizing social context information, which can effectively improve the performance of microblog sentiment classification.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Immunology
Liviu-Adrian Cotfas, Liliana Craciun, Camelia Delcea, Margareta Stela Florescu, Erik-Robert Kovacs, Anca Gabriela Molanescu, Mihai Orzan
Summary: This paper analyzes English tweets posted worldwide during two different time periods following the announcement of the Delta and Omicron variants of COVID-19. By using a language model, the study detects tweets expressing vaccine hesitancy and analyzes the reasons behind it. The findings show an increase in hesitant tweets from 4.31% during the Delta period to 11.22% during the Omicron period, accompanied by a decrease in the number of reasons for vaccine hesitancy. This raises concerns about the effectiveness of vaccination information campaigns.
Review
Computer Science, Artificial Intelligence
Chinmayee Sahoo, Mayur Wankhade, Binod Kumar Singh
Summary: This research article provides a comprehensive review of using deep learning techniques in sentiment analysis. It covers various aspects of sentiment analysis, including data preprocessing, feature extraction, model architectures, and evaluation metrics. The study explores the application of recurrent neural networks, convolutional neural networks, and transformer models in sentiment analysis, as well as the utilization of long short-term memory and gated recurrent unit to model sequential dependencies in text data. The findings from this review can aid in the development of more accurate and efficient sentiment analysis models, benefiting organizations in gaining insights from large volumes of textual data in various domains.
INTERNATIONAL JOURNAL OF MULTIMEDIA INFORMATION RETRIEVAL
(2023)
Article
Computer Science, Artificial Intelligence
Xiaofei Zhu, Jie Wua, Ling Zhu, Jiafeng Guo, Ran Yu, Katarina Boland, Stefan Dietze
Summary: This study presents a novel neural microblog sentiment classification method that utilizes user’s contextual and historical state information to learn informative representations of microblog posts and alleviate the context sparsity problem. Experimental results demonstrate superior performance compared to state-of-the-art baselines in microblog sentiment analysis.
Article
Psychology, Multidisciplinary
Agnes Buvar, Agnes Zsila, Gabor Orosz
Summary: Social media influencers can raise awareness for sustainability and establish norms related to a more sustainable lifestyle. The study explores the impact of authenticity and expert opinion on credibility, as well as the presence of dynamic norms, on the perceived credibility of posts.
FRONTIERS IN PSYCHOLOGY
(2023)
Article
Computer Science, Interdisciplinary Applications
Lydia Bryan-Smith, Jake Godsall, Franky George, Kelly Egode, Nina Dethlefs, Dan Parsons
Summary: Traditional flood modelling relies on costly hydrodynamic physical simulations, while social media platforms like Twitter are used for real-time communication during flooding events. This article introduces a novel flood forecasting and monitoring model that uses a transformer network to analyze the sentiment of multimodal inputs (text and images) in order to assess flooding severity. The article also compares state-of-the-art deep learning methods for image and natural language processing. Additionally, the article demonstrates the effective use of information from tweets to dynamically visualize detailed geographical flood-related information.
COMPUTERS & GEOSCIENCES
(2023)
Article
Computer Science, Information Systems
Mayur Wankhade, Chandra Sekhara Rao Annavarapu, Ajith Abraham
Summary: Sentiment classification is a crucial task in natural language processing. This research investigates the impact of text preprocessing techniques on sentiment classification and proposes a novel framework called CBMAFM that leverages the synergistic power of CNN and BiLSTM through a multi-attention fusion mechanism. The framework preserves both local and global context dependencies, resulting in improved performance compared to other state-of-the-art methods.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
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)
