Article
Computer Science, Artificial Intelligence
L. D. C. S. Subhashini, Yuefeng Li, Jinglan Zhang, Ajantha S. Atukorale, Yutong Wu
Summary: With the increasing number of customer reviews on the Web, there is a growing demand for effective methods to retrieve valuable information from reviews. Researchers have proposed many automatic mining and classification methods, but choosing a trusted method remains a challenge for companies. This article surveys recent opinion mining literature, focusing on text feature extraction, knowledge representation, and classification methods.
ARTIFICIAL INTELLIGENCE REVIEW
(2021)
Article
Chemistry, Multidisciplinary
Ling Wang, Minglei Shan, Tie Hua Zhou, Keun Ho Ryu
Summary: Accurately identifying medical entities and extracting entity relationships has become a hot topic, and through knowledge mining, a deeper understanding of the complex relationships within diseases can be gained, providing significant guidance for clinical research.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Information Systems
Qifeng Sun, Jiayue Xu, Youxiang Duan, Peiying Zhang, Nan Jiang, Anas Ratib Alsoud, Laith Abualigah
Summary: Word similarity computation is a fundamental research area in semantic information processing. Most prior studies on Chinese word similarity computation have used rule-based methods, while English word similarity computation has relied on WordNet. We cannot directly use English methods for Chinese word similarity computation. Therefore, we develop an improved Chinese method by incorporating various parameters into the computation process. We also utilize a hierarchical ontology knowledge base to improve accuracy. Our experimental results demonstrate that our method outperforms state-of-the-art approaches.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Theory & Methods
Fethi A. Rabhi, Madhushi Bandara, Kun Lu, Saif Dewan
Summary: This paper presents the design of an innovative Information Technology (IT) platform that enables data sharing and makes analytics knowledge readily available and modifiable, supporting day-to-day decision making. Organizing analytics knowledge around the concept of a research variable, this platform is particularly suitable for developing empirical data analytics applications in any domain.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2021)
Article
Computer Science, Information Systems
Belgacem Brahimi, Mohamed Touahria, Abdelkamel Tari
Summary: This paper proposes methods to extract valuable opinions from online movie reviews using n-gram and skip-n-gram models, subjective words, and feature reduction techniques to enhance sentiment analysis in Arabic. Experimental results demonstrate the effectiveness of these methods in improving sentiment classification results.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2021)
Review
Computer Science, Information Systems
Ankita Sharma, Udayan Ghose
Summary: While sentiment analysis in English has made significant progress, research on sentiment analysis in Hindi is still in its early stages. This study focuses on sentiment classification of Hindi movie reviews, compiling a dataset of 10K annotated reviews and identifying challenges specific to the Hindi language. Various machine learning approaches, including ensemble-based classifiers, are explored and evaluated, with a stacked ensemble-based architecture (SEBA) showing superior performance. SEBA achieves high accuracy, precision, recall, and F1-score on the HLMR dataset, indicating its effectiveness for online deployment in binary review classification tasks.
Article
Computer Science, Artificial Intelligence
Baris Ozyurt, M. Ali Akcayol
Summary: With the widespread use of social networks and other platforms, the volume of user-generated textual data is growing rapidly, making sentiment analysis and opinion mining in user reviews more and more important. To tackle issues like data sparsity and lack of co-occurrence patterns, studies have proposed methods like SS-LDA to adapt LDA for short texts. Experimental results indicate that SS-LDA performs competitively in extracting product aspects.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Chemistry, Multidisciplinary
Su Yang, Farzin Deravi
Summary: This paper proposes a novel re-engineering mechanism for generating word embeddings to enhance document-level sentiment analysis. By re-engineering the feature components of embedding vectors, the mechanism increases the between-class separation and leverages the informative content of the documents.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Interdisciplinary Applications
Liang Guo, Fu Yan, Tian Li, Tao Yang, Yuqian Lu
Summary: The traditional construction of process knowledge base is non-automated and time-consuming, which requires manual work and may lead to ambiguity in knowledge representation. This paper introduces an automatic construction framework based on knowledge graph (KG), which involves steps like classification, annotation, extraction, and representation to improve the efficiency and quality of the process knowledge base.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2022)
Article
Computer Science, Artificial Intelligence
Mansoor Ahmed, Kainat Ansar, Cal B. Muckley, Abid Khan, Adeel Anjum, Muhammad Talha
Summary: Digital fraud has impacted ordinary consumers and the finance industry, leading to efforts to improve fraud detection and deterrence capabilities. The proposed solution in this study involves an Intimation Rule Based alert generation algorithm, utilizing a rich domain knowledge base and rule-based reasoning.
PEERJ COMPUTER SCIENCE
(2021)
Review
Computer Science, Software Engineering
Bin Lin, Nathan Cassee, Alexander Serebrenik, Gabriele Bavota, Nicole Novielli, Michele Lanza
Summary: Opinion mining, also known as sentiment analysis, has gained attention in software engineering research for identifying developer emotions and extracting user criticisms in mobile apps. Through a systematic literature review of 185 papers, we provide well-defined categories of opinion mining-related software development activities, available opinion mining approaches, datasets for evaluation and tool customization, and concerns or limitations for researchers to consider when applying or customizing opinion mining techniques. Our study serves as a reference for selecting suitable opinion mining tools and provides critical insights for the further development of this technique in software engineering.
ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY
(2022)
Article
Computer Science, Theory & Methods
Shalini Saini, Nitesh Saxena
Summary: This article explores the security, integrity, and credibility issues of medical artificial intelligence (MedAI) tools, highlighting the threat of predatory research to the integrity of MedAI inputs and the resulting lack of trust in MedAI output. It provides a comprehensive literature review on the threats of data pollution, feasible attacks, and the influence on healthcare in research literature, aiming to address the vulnerabilities of predatory research in MedAI solutions and contribute to the development of robust MedAI solutions in the future.
ACM COMPUTING SURVEYS
(2023)
Article
Computer Science, Artificial Intelligence
Li Yang, Jieming Wang, Jin-Cheon Na, Jianfei Yu
Summary: Multimodal entity-category-sentiment triple extraction (MECSTE) is an emerging task in sentiment analysis, aiming to simultaneously extract entities, fine-grained entity categories, and sentiment polarities from sentences and images. Previous studies have overlooked the interconnection among subtasks and failed to provide sufficient information for disambiguating entities. This study proposes a generative multimodal approach and demonstrates its superiority through experiments on annotated Twitter datasets.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Lan You, Fanyu Han, Jiaheng Peng, Hong Jin, Christophe Claramunt
Summary: This paper introduces a sentiment knowledge-adaptive pretraining model (ASK-RoBERTa) that predicts sentiment polarities of different aspects by building a sentiment word dictionary and optimizing mining rules. The experimental results on multiple public benchmark datasets demonstrate the satisfactory performance of ASK-RoBERTa.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Giuseppe D'Aniello, Matteo Gaeta, Ilaria La Rocca
Summary: This article presents an overview of techniques and approaches for aspect-based sentiment analysis (ABSA) and highlights the main issues in this field. The KnowMIS-ABSA model is proposed, which emphasizes that sentiment, affect, emotion, and opinion are different concepts and should be measured using different tools and metrics.
ARTIFICIAL INTELLIGENCE REVIEW
(2022)
Article
Computer Science, Artificial Intelligence
Siddique Latif, Heriberto Cuayahuitl, Farrukh Pervez, Fahad Shamshad, Hafiz Shehbaz Ali, Erik Cambria
Summary: This article provides a comprehensive survey on the progress of deep reinforcement learning (DRL) in the audio domain. By examining research studies in areas such as speech and music, the article discusses the methods and applications of DRL, and highlights the challenges and open areas for future research in the audio domain.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Computer Science, Artificial Intelligence
Jinjie Ni, Tom Young, Vlad Pandelea, Fuzhao Xue, Erik Cambria
Summary: This article surveys deep learning based dialogue systems, comprehensively reviewing and analyzing the state-of-the-art research outcomes in this field. It discusses different models in terms of principles, characteristics, and applications, and explores task-oriented and open-domain dialogue systems as two streams of research. The article also reviews evaluation methods and datasets for dialogue systems, and identifies possible future research trends.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Computer Science, Artificial Intelligence
Ankita Gandhi, Kinjal Adhvaryu, Soujanya Poria, Erik Cambria, Amir Hussain
Summary: This survey paper explores the importance and recent advancements in sentiment analysis and multimodal sentiment analysis in the fields of artificial intelligence and natural language processing. It compares various fusion architectures in terms of MSA categories and presents interdisciplinary applications and future research directions.
INFORMATION FUSION
(2023)
Article
Computer Science, Artificial Intelligence
Tian-Hui You, Ling-Ling Tao, Erik Cambria
Summary: This study proposes a hotel ranking model based on online textual reviews, considering the differences in the number of reviews on different aspects. The model utilizes sentiment analysis to assist tourists in making desirable decisions on hotel selection.
INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING
(2023)
Article
Computer Science, Artificial Intelligence
Xiaoshi Zhong, Erik Cambria
Summary: Time information is crucial in the fields of data mining, information retrieval, and natural language processing. Time expression recognition and normalization (TERN) serves as a fundamental task for other linguistic tasks. This survey reviews previous research, provides an overview of time expression analysis development, and explores the role of time expressions in different domains. Three methods for TERN development are discussed: rule-based, traditional machine-learning, and deep-learning. Additionally, useful datasets, software, and potential future research directions are outlined.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Computer Science, Artificial Intelligence
Xulang Zhang, Rui Mao, Erik Cambria
Summary: Computational syntactic processing is a fundamental technique in natural language processing that transforms natural language into structured texts with syntactic features. This work surveys low-level syntactic processing techniques such as normalization, sentence boundary disambiguation, part-of-speech tagging, text chunking, and lemmatization, categorizes widely used methods, investigates challenges, and proposes future research directions.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Computer Science, Artificial Intelligence
Jingfeng Cui, Zhaoxia Wang, Seng-Beng Ho, Erik Cambria
Summary: Sentiment analysis, a research hotspot in natural language processing, has attracted significant attention and resulted in a growing number of research papers. Despite numerous literature reviews on sentiment analysis, there has been no dedicated survey examining the evolution of research methods and topics. This study fills this gap by conducting a comprehensive survey that combines keyword co-occurrence analysis and community detection algorithm. The survey compares and analyzes the connections between research methods and topics over the past two decades and uncovers hotspots and trends over time, providing valuable guidance for researchers. Furthermore, the paper offers practical insights, technical directions, limitations, and future research prospects in sentiment analysis.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Computer Science, Information Systems
Kelvin Du, Frank Xing, Erik Cambria
Summary: Combining symbolic and subsymbolic methods has emerged as a promising strategy in tackling increasingly complex AI research tasks. This study presents a targeted aspect-based financial sentiment analysis hybrid model that incorporates multiple lexical knowledge sources into the fine-tuning process of pre-trained transformer models. Experimental results demonstrate that knowledge-enabled models systematically improve aspect sentiment analysis performance and even outperform state-of-the-art results.
ACM TRANSACTIONS ON MANAGEMENT INFORMATION SYSTEMS
(2023)
Article
Computer Science, Cybernetics
Luna Ansari, Shaoxiong Ji, Qian Chen, Erik Cambria
Summary: Changes in human lifestyle have led to an increase in depression cases. Automated detection methods are effective in identifying depressed individuals. Ensemble models outperform hybrid models for depression detection.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Yu Ma, Rui Mao, Qika Lin, Peng Wu, Erik Cambria
Summary: This study proposes a novel Multi-source Aggregated Classification (MAC) method for predicting stock price movements. MAC incorporates numerical features, market-driven news sentiments, and sentiments of related stocks to better represent real market sentiments. The method also utilizes a graph convolutional network to capture the effects of news from related companies on the target stock. Extensive experiments demonstrate that MAC outperforms state-of-the-art models in predicting stock price movements, Sharpe Ratio, and backtesting trading incomes.
INFORMATION FUSION
(2023)
Article
Computer Science, Information Systems
Qian Liu, Rui Mao, Xiubo Geng, Erik Cambria
Summary: This paper conducts a systematic empirical study on semantic matching in machine reading comprehension (MRC). It formulates a two-stage framework and compares different setups of semantic matching modules on four MRC datasets. The study finds that semantic matching improves the effectiveness and efficiency of MRC, especially for answering questions with noisy and adversarial context. Matching coarse-grained context to questions and using semantic matching modules is more effective than fine-grained context matching, such as sentences and spans. However, semantic matching decreases the performance on why questions, suggesting that it is more helpful for questions that can be answered by retrieving information from a single sentence.
INFORMATION PROCESSING & MANAGEMENT
(2023)
Article
Computer Science, Interdisciplinary Applications
Phuong Le-Hong, Erik Cambria
Summary: This paper introduces a semantics-aware approach to natural language inference, in which explicit lexical and concept-level semantics from knowledge bases are incorporated to improve inference accuracy of neural network models. The authors conduct an extensive evaluation of four models with different sentence encoders, and experimental results show that semantics-aware neural models achieve higher accuracy than those without semantics information. On average of the three strong models, the proposed approach improves natural language inference in different languages.
LANGUAGE RESOURCES AND EVALUATION
(2023)
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
Education & Educational Research
Iti Chaturvedi, Erik Cambria, Roy E. Welsch
Summary: Video conferencing enables synchronous communication in classrooms and stimulates learners with multi-sensory content. Constructive pedagogy is used to teach complex AI equations, allowing students to experiment with different problem-solving methods. Multiple-choice questions provide reliable and quick assessments of student skill levels. The Australian Computer Society accreditation ensures flexible teaching templates for each subject. Geographic constraints necessitate subordinate campuses to be affiliated with a main campus. Continuity in learning and assessments between different subjects is maintained through the concept of strands. Feedback from students using AI-based simulations showed challenges in understanding lectures and assignments, hence a Kahoot quiz was introduced to measure learning. Charts were used to aid students with vision or attention-related disorders in visually observing variables and analyzing in-depth. Real-world industry examples were incorporated into lectures to enhance employability.
EDUCATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Deeksha Varshney, Asif Ekbal, Erik Cambria
Summary: This paper focuses on the neural-based interactive dialogue system that aims to engage and retain humans in long-lasting conversations. It proposes a new neural generative model that combines step-wise co-attention, self-attention-based transformer network, and an emotion classifier to control emotion and knowledge transfer during response generation. The results from quantitative, qualitative, and human evaluation show that the proposed models can generate natural and coherent sentences, capturing essential facts with significant improvement over emotional content.
KNOWLEDGE-BASED SYSTEMS
(2024)
