Article
Computer Science, Information Systems
Mehdi Yekrangi, Nikola S. Nikolov
Summary: This study aims to identify the most effective embedding techniques and classification algorithms for sentiment analysis in financial markets. By using a heterogeneous corpus and various pre-trained embeddings, along with an optimized embedding layer, the study presents a sentiment analysis model with robust performance.
Article
Mathematics, Applied
Tingting Li, Chao Luo
Summary: Financial markets are often influenced by external factors such as breaking news or important economic data releases, which can lead to increased volatility. This article combines complex network theory with granular computing to study the propagation of fluctuation patterns in financial markets after abnormal changes occur, and empirical studies are conducted to verify the validity of theoretical analysis.
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION
(2021)
Article
Computer Science, Information Systems
Shixuan Li, Wenxuan Shi, Jiancheng Wang, Heshen Zhou
Summary: This study aims to construct a Chinese financial domain sentiment lexicon and apply it to financial distress prediction using a deep learning-based framework. The experiment results show that the deep learning-based models can generate a satisfactory CFDSL and sentiment features calculated four years prior to the predicted benchmark year achieve optimum performance in FDP.
INFORMATION PROCESSING & MANAGEMENT
(2021)
Review
Computer Science, Artificial Intelligence
Vipin Jain, Kanchan Lata Kashyap
Summary: In this work, a systematic literature review is presented to analyze the sentiment of the Indian population towards COVID-19 and its vaccination. The review includes 40 publications from January 2020 to August 2022, obtained from four primary databases. The challenges faced by authors in collecting datasets are analyzed, and lexical, machine, and deep learning techniques are found to be commonly used for sentiment analysis. The performance of these techniques is comparatively analyzed, and future research directions and recommendations are highlighted.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2023)
Article
Computer Science, Information Systems
Zulqurnain Ali, Abdul Razzaq, Sajid Ali, Sulman Qadri, Azam Zia
Summary: Social media platforms are valuable sources of information through user-generated reviews and comments. Identification and extraction of subjective information from text is a crucial challenge in sentiment analysis. A Prescriptive Sentiment Analysis (PSA) based on features synchronization has been introduced to increase accuracy in text sentiment analysis, showing significant improvement in sentiment analysis efficiency compared to other baseline methods.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
Article
Computer Science, Interdisciplinary Applications
Pedro Ruas, Francisco M. Couto
Summary: This paper introduces a model, NILINKER, which includes a candidate retrieval module for biomedical NIL entities and a neural network that leverages the attention mechanism to find relevant concepts from target Knowledge Bases. It also provides a new evaluation dataset, EvaNIL, for training and evaluating models focusing on the NIL entity linking task. Experimental results show that NILINKER is able to improve the performance of the Named Entity Linking model.
JOURNAL OF BIOMEDICAL INFORMATICS
(2022)
Article
Business, Finance
Rongda Chen, Shengnan Wang, Chenglu Jin, Jingjing Yu, Xinyu Zhang, Shuonan Zhang
Summary: This study establishes a multidimensional investor sentiment measure for Internet financial products (ISIFP) using text mining and WeChat subscription data in China. The study finds that different sentiments have varying impacts on market risk and expected returns.
INTERNATIONAL REVIEW OF FINANCIAL ANALYSIS
(2023)
Article
Psychology, Multidisciplinary
Ming Yang, Binghan Jiang, Yimin Wang, Tianyu Hao, Yuankun Liu
Summary: The purpose of business sentiment analysis is to determine the emotions or attitudes expressed towards a company, products, services, personnel, or events. Text analysis is currently the simplest and most developed method for sentiment analysis. However, text-based analysis still faces challenges in recognizing double meanings, jokes, and allusions, as well as explaining regional and non-native language differences. To address these issues, this article proposes the use of an undirected weighted graph and a convolutional neural network in a news mining-based business sentiment analysis framework.
FRONTIERS IN PSYCHOLOGY
(2022)
Review
Computer Science, Theory & Methods
Wenbo Ge, Pooia Lalbakhsh, Leigh Isai, Artem Lenskiy, Hanna Suominen
Summary: This article examines the current state of financial volatility forecasting and identifies several issues and potential solutions. It also provides background information for the field of neural network volatility forecasting.
ACM COMPUTING SURVEYS
(2023)
Article
Computer Science, Artificial Intelligence
Herman M. Wandabwa, M. Asif Naeem, Farhaan Mirza, Russel Pears
Summary: Consumption of content in short-text microblogs is influenced by individual users and their social network interests, which change dynamically over time. Detecting semantic changes is important for mapping user profiles, especially in platforms with limited user data. A model was used to validate interest changes over time, achieving a high Pearson correlation coefficient for interest change verification.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Chemistry, Multidisciplinary
Lifang Wu, Sinuo Deng, Heng Zhang, Ge Shi
Summary: Sentiment analysis is a challenging task due to the affective gap in accurately extracting sentimental features from visual contents. Previous approaches neglect the interaction among objects and may introduce noisy features. In this paper, a method called Sentiment Interaction Distillation (SID) Network is proposed to guide feature learning using object sentimental interaction. Experimental results show that the reasonable use of interaction features can improve the performance of sentiment analysis.
APPLIED SCIENCES-BASEL
(2022)
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
Thomas Bos, Flavius Frasincar
Summary: This research explores automatic approaches to build financial sentiment lexicons, introducing weighted Pointwise Mutual Information methods and methods for considering negation to improve the sentiment lexicon construction process. The results show that the financial sentiment lexicons perform well in sentiment classification tasks, especially when negation is taken into account.
COGNITIVE COMPUTATION
(2022)
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)
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
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
Javier Torregrosa, Sergio D'Antonio-Maceiras, Guillermo Villar-Rodriguez, Amir Hussain, Erik Cambria, David Camacho
Summary: Political tensions have increased in Europe since the beginning of the new century, leading to social movements and political changes in various countries. This study examines the political discourse and underlying tensions during Madrid's elections in May 2021, using a mixed methodology approach. The findings suggest that the electoral campaign is not as negative as perceived by the citizens, and that ideologically extreme parties tend to use more aggressive language.
COGNITIVE COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Wei Sun, Shaoxiong Ji, Erik Cambria, Pekka Marttinen
Summary: Human coders assign standardized medical codes to clinical documents, but it is prone to errors and requires significant effort. Automated medical coding methods using machine learning, such as deep neural networks, have been developed. However, challenges still exist due to code association complexity, noise in lengthy documents, and imbalanced class problem. In this study, we propose a novel neural network model called the Multitask Balanced and Recalibrated Neural Network to address these issues. Experiments on a real-world clinical dataset called MIMIC-III demonstrate that our model outperforms competitive baselines.
ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
(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, Artificial Intelligence
Qika Lin, Rui Mao, Jun Liu, Fangzhi Xu, Erik Cambria
Summary: Knowledge graph completion (KGC) is crucial for many downstream applications. Existing language model-based methods for KGC often overlook the importance of modeling the deeper semantic information, such as topology contexts and logical rules. In this paper, we propose a unified framework FTL-LM that effectively incorporates topology contexts and logical rules in language models, and experimental results demonstrate its superiority over the state-of-the-art methods.
INFORMATION FUSION
(2023)
Article
Computer Science, Information Systems
Erik Cambria, Lorenzo Malandri, Fabio Mercorio, Mario Mezzanzanica, Navid Nobani
Summary: The field of explainable artificial intelligence (XAI) has gained increasing importance in recent years. However, existing research often overlooks the role of natural language in generating explanations. This survey reviews 70 XAI papers published between 2006 and 2021 and evaluates their readiness in terms of natural language explanations. The results show that only a few recent studies have considered using natural language for communication with end users or implemented methods for generating natural language explanations.
INFORMATION PROCESSING & MANAGEMENT
(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
Ruicheng Liu, Rui Mao, Anh Tuan Luu, Erik Cambria
Summary: The task of resolving repeated objects in natural languages, known as coreference resolution, is an important part of modern natural language processing. It is classified into entity coreference resolution and event coreference resolution based on the resolved objects. Predicting coreference connections and identifying mentions/triggers are the major challenges in coreference resolution due to the difficulty of implicit relationships in natural language understanding. In this survey, we review the current employed evaluation metrics, datasets, and methods, investigating 10 widely used metrics, 18 datasets, and 4 main technical trends. We believe that this work provides a comprehensive roadmap for understanding the past and the future of coreference resolution.
ARTIFICIAL INTELLIGENCE REVIEW
(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
Computer Science, Artificial Intelligence
Zhaoxia Wang, Zhenda Hu, Fang Li, Seng-Beng Ho, Erik Cambria
Summary: This research proposes a 3-phase hybrid model that utilizes both technical indicators and social media text sentiments as influence factors for stock trending prediction. The result shows that the proposed method has an accuracy of 73.41% and F1-score of 84.19%. The research not only demonstrates the merits of the proposed method, but also indicates that integrating social opinions with technical indicators is a right direction for enhancing the performance of learning-based stock market trending analysis methods.
COGNITIVE COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Mostafa M. Amin, Erik Cambria, Bjoern W. Schuller
Summary: The employment of foundation models is expanding and ChatGPT has the potential to enhance existing NLP techniques with its novel knowledge.
IEEE INTELLIGENT SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Wei Li, Yang Li, Vlad Pandelea, Mengshi Ge, Luyao Zhu, Erik Cambria
Summary: The paper introduces a new task called emotion-cause pair extraction in conversations (ECPEC), which aims to extract pairs of emotional utterances and corresponding cause utterances in conversations. The utterance-level ECPEC task is more challenging as the distance between emotion and cause utterances is greater. The experimental results on the proposed ConvECPE dataset demonstrate the feasibility of the ECPEC task and the effectiveness of the framework.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(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)
Article
Computer Science, Artificial Intelligence
Hao Yang, Min Wang, Zhengfei Yu, Hang Zhang, Jinshen Jiang, Yun Zhou
Summary: In this paper, a novel method called CSTTA is proposed for test time adaptation (TTA), which utilizes confidence-based optimization and sample reweighting to better utilize sample information. Extensive experiments demonstrate the effectiveness of the proposed method.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Jin Liu, Ju-Sheng Mi, Dong-Yun Niu
Summary: This article focuses on a novel method for generating a canonical basis for decision implications based on object-induced operators (OE operators). The logic of decision implication based on OE operators is described, and a method for obtaining the canonical basis for decision implications is given. The completeness, nonredundancy, and optimality of the canonical basis are proven. Additionally, a method for generating true premises based on OE operators is proposed.
KNOWLEDGE-BASED SYSTEMS
(2024)
Review
Computer Science, Artificial Intelligence
Kun Bu, Yuanchao Liu, Xiaolong Ju
Summary: This paper discusses the importance of sentiment analysis and pre-trained models in natural language processing, and explores the application of prompt learning. The research shows that prompt learning is more suitable for sentiment analysis tasks and can achieve good performance.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Xiangjun Cai, Dagang Li
Summary: This paper presents a new decomposition mechanism based on learned decomposition mapping. By using a neural network to learn the relationship between original time series and decomposed results, the repetitive computation overhead during rolling decomposition is relieved. Additionally, extended mapping and partial decomposition methods are proposed to alleviate boundary effects on prediction performance. Comparative studies demonstrate that the proposed method outperforms existing RDEMs in terms of operation speed and prediction accuracy.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Xu Wu, Yang Liu, Jie Tian, Yuanpeng Li
Summary: This paper proposes a blockchain-based privacy-preserving trust management architecture, which adopts federated learning to train task-specific trust models and utilizes differential privacy to protect device privacy. In addition, a game theory-based incentive mechanism and a parallel consensus protocol are proposed to improve the accuracy of trust computing and the efficiency of consensus.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Zaiyang Yu, Prayag Tiwari, Luyang Hou, Lusi Li, Weijun Li, Limin Jiang, Xin Ning
Summary: This study introduces a 3D view-based approach that effectively handles occlusions and leverages the geometric information of 3D objects. The proposed method achieves state-of-the-art results on occluded ReID tasks and exhibits competitive performance on holistic ReID tasks.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Yongliang Shi, Runyi Yang, Zirui Wu, Pengfei Li, Caiyun Liu, Hao Zhao, Guyue Zhou
Summary: Neural implicit representations have gained attention due to their expressive, continuous, and compact properties. However, there is still a lack of research on city-scale continual implicit dense mapping based on sparse LiDAR input. In this study, a city-scale continual neural mapping system with a panoptic representation is developed, incorporating environment-level and instance-level modeling. A tailored three-layer sampling strategy and category-specific prior are proposed to address the challenges of representing geometric information in city-scale space and achieving high fidelity mapping of instances under incomplete observation.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Ruihan Hu, Zhi-Ri Tang, Rui Yang, Zhongjie Wang
Summary: Mesh data is crucial for 3D computer vision applications worldwide, but traditional deep learning frameworks have struggled with handling meshes. This paper proposes MDSSN, a simple mesh computation framework that models triangle meshes and represents their shape using face-based and edge-based Riemannian graphs. The framework incorporates end-to-end operators inspired by traditional deep learning frameworks, and includes dedicated modules for addressing challenges in mesh classification and segmentation tasks. Experimental results demonstrate that MDSSN outperforms other state-of-the-art approaches.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Buliao Huang, Yunhui Zhu, Muhammad Usman, Huanhuan Chen
Summary: This paper proposes a novel semi-supervised conditional normalizing flow (SSCFlow) algorithm that combines unsupervised imputation and supervised classification. By estimating the conditional distribution of incomplete instances, SSCFlow facilitates imputation and classification simultaneously, addressing the issue of separated tasks ignoring data distribution and label information in traditional methods.
KNOWLEDGE-BASED SYSTEMS
(2024)
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)
Article
Computer Science, Artificial Intelligence
Junchen Ye, Weimiao Li, Zhixin Zhang, Tongyu Zhu, Leilei Sun, Bowen Du
Summary: Modeling multivariate time series has long been a topic of interest for scholars in various fields. This paper introduces MvTS, an open library based on Pytorch, which provides a unified framework for implementing and evaluating these models. Extensive experiments on public datasets demonstrate the effectiveness and universality of the models reproduced by MvTS.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Reham R. Mostafa, Ahmed M. Khedr, Zaher Al Aghbari, Imad Afyouni, Ibrahim Kamel, Naveed Ahmed
Summary: Feature selection is crucial in classification procedures, but it faces challenges in high-dimensional datasets. To overcome these challenges, this study proposes an Adaptive Hybrid-Mutated Differential Evolution method that incorporates the mechanics of the Spider Wasp Optimization algorithm and the concept of Enhanced Solution Quality. Experimental results demonstrate the effectiveness of the method in terms of accuracy and convergence speed, and it outperforms contemporary cutting-edge algorithms.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Ti Xiang, Pin Lv, Liguo Sun, Yipu Yang, Jiuwu Hao
Summary: This paper introduces a Track Classification Model (TCM) based on marine radar, which can effectively recognize and classify shipping tracks. By using a feature extraction network with multi-feature fusion and a dataset production method to address missing labels, the classification accuracy is improved, resulting in successful engineering application in real scenarios.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Zhihao Zhang, Yuan Zuo, Chenghua Lin, Junjie Wu
Summary: This paper proposes a novel unsupervised context-aware quality phrase mining framework called LMPhrase, which is built upon large pre-trained language models. The framework mines quality phrases as silver labels using a parameter-free probing technique on the pre-trained language model BERT, and formalizes the phrase tagging task as a sequence generation problem by fine-tuning on the Sequence to-Sequence pre-trained language model BART. The results of extensive experiments show that LMPhrase consistently outperforms existing competitors in two different granularity phrase mining tasks.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Kemal Buyukkaya, M. Ozan Karsavuran, Cevdet Aykanat
Summary: The study aims to investigate the hybrid parallelization of the Stochastic Gradient Descent (SGD) algorithm for solving the matrix completion problem on a high-performance computing platform. A hybrid parallel decentralized SGD framework with asynchronous inter-process communication and a novel flexible partitioning scheme is proposed to achieve scalability up to hundreds of processors. Experimental results on real-world benchmark datasets show that the proposed algorithm achieves 6x higher throughput on sparse datasets compared to the state-of-the-art, while achieving comparable throughput on relatively dense datasets.
KNOWLEDGE-BASED SYSTEMS
(2024)