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Article
Computer Science, Artificial Intelligence
Wei Li et al.
Summary: This paper presents a fast, compact, and parameter-efficient framework for conversational sentiment analysis, which outperforms the state of the art in most cases according to extensive experiments on three standard datasets.
Article
Computer Science, Artificial Intelligence
Faliang Huang et al.
Summary: This paper proposes a novel model named AEC-LSTM to improve LSTM networks by integrating emotional intelligence (EI) and attention mechanism, aiming to enhance the learning of text sentiment features and improve sentiment classification performance effectively.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Eva Zangerle et al.
Summary: This study analyzes the connection between users' emotional states and their musical choices, finding that affective information has a significant impact on music recommendation. Different ranking strategies are proposed based on emotional information and latent features, with those incorporating affective information and leveraging hashtags outperforming others in capturing context-specific preferences.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2021)
Article
Computer Science, Information Systems
Ruby Rani et al.
Summary: Automatic text summarization for Hindi novels and stories is a challenging task due to the lack of resources and processing tools. A proposed model using extractive lexical knowledge-rich topic modeling successfully generates concise, articulate, and coherent summaries, showcasing optimal results through evaluation metrics such as gist diversity, retention ratio, and ROUGE score.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Ramesh Chandra Belwal et al.
Summary: In this paper, a new graph-based summarization technique is proposed, which not only considers the similarity among individual sentences, but also takes into account the similarity between sentences and the overall document topic. The weight assigned to the edges of the graph considers both the similarity among nodes and the similarity to the topics of the overall document. By incorporating semantic measure to find node similarity, the proposed method shows significant improvement in summary quality compared to existing text summarization techniques.
JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Biswarup Ray et al.
Summary: The study introduces a hotel recommendation system based on sentiment analysis and aspect-based review categorization, using machine learning models and algorithms to achieve high accuracy and classification performance.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Theory & Methods
Chenquan Gan et al.
Summary: This paper proposes a scalable multi-channel dilated joint architecture of convolutional neural network and bidirectional long short-term memory model with an attention mechanism to analyze the sentiment tendency of Chinese texts. The model can extract both the original context features and the multiscale high-level context features, and utilize the attention mechanism to further distinguish the difference of features. Additionally, an adaptive weighted loss function is designed to effectively avoid the imbalance of classes in training data.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2021)
Article
Computer Science, Theory & Methods
Mohammad Ehsan Basiri et al.
Summary: ABCDM is an attention-based bidirectional CNN-RNN deep model that utilizes bidirectional LSTM and GRU to extract contextual information from both past and future, applying attention mechanism to emphasize different words for sentiment analysis polarity detection.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2021)
Article
Computer Science, Artificial Intelligence
Wei Song et al.
Summary: With the rise of user-generated content on the internet, sentiment analysis of text has gained significant attention as a research field. Aspect-based sentiment analysis (ABSA) is a detailed approach in analyzing sentiments towards specific aspects within context sentences. Most ABSA methods rely on recurrent neural networks (RNNs) and attention mechanisms, but these techniques have limitations in handling long-distance dependencies and local semantic information effectively. This paper proposes a Semantics Perception and Refinement Network (SPRN) that outperforms 14 state-of-the-art ABSA methods in accuracy, marco-F1, and AUROC metrics through the application of a Dual Gated Multichannel Convolution (DGMCC) structure and a Dual Refinement Gate (DRG) for aspect-related semantic feature extraction and noise filtering.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Srishti Vashishtha et al.
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)
Review
Computer Science, Information Systems
Huiliang Zhao et al.
Summary: Recent development of internet technology has made online shopping a mainstream shopping method. Sentiment Analysis (SA) of user reviews on e-commerce platforms can effectively improve user satisfaction. A new optimized Machine Learning (ML) algorithm called LSIBA-ENN is proposed for SA of online product reviews, which outperforms existing algorithms in sentiment classification.
INFORMATION PROCESSING & MANAGEMENT
(2021)
Article
Computer Science, Artificial Intelligence
Anping Zhao et al.
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
Xiaodong Li et al.
Summary: The aim of automatic text summarization is to extract representative texts while maintaining major points consistent with the original documents. However, the issue of sentimental information loss is commonly ignored in existing studies. To address this, a sentiment compensation mechanism and a graph-based approach named SLS are proposed. Experimental results show that SLS outperforms baselines in sentiment retention and prediction accuracy.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Marouane Birjali et al.
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
Automation & Control Systems
Xinzhi Wang et al.
Summary: Previous research in artificial intelligence has primarily focused on recognizing emotions rather than exploring reasons for incorrect emotional recognition. Through the analysis of natural language text from web news, the study reveals correlations among emotions and potential cognitive biases. Findings suggest that in subjective comments, emotions are easily mistaken for anger.
IEEE TRANSACTIONS ON CYBERNETICS
(2021)
Article
Computer Science, Artificial Intelligence
Valerio Basile et al.
Summary: This article discusses the experience in organizing SENTIPOLC in evaluation campaigns, introducing the shared task of sentiment classification of Italian tweets, along with the datasets, evaluation methodology, and approaches and results of participating systems. The article also reflects on the challenges of state-of-the-art systems for sentiment analysis of microblogging in Italian, and shares the lessons learned from running this task for two consecutive editions.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2021)
Article
Computer Science, Information Systems
Myeongjun Jang et al.
Summary: Summarization is an important technique that condenses source documents to create a coherent summary, and deep learning has improved this field significantly. However, deep neural network-based models have limitations such as requiring a large amount of labeled data. In this study, a new unsupervised summarization model called LFIP-SUM is proposed, which does not require parameter training and achieves acceptable performance.
Article
Computer Science, Artificial Intelligence
Rajendra Kumar Roul
Summary: The paper introduces a novel methodology for text summarization using topic modeling and classification technique to generate coherent summaries. It proposes a heuristic approach to determine the number of topics in a corpus and condenses a large set of sentences into a concise summary. The model arranges sentences based on their importance, showing promising results compared to existing text summarization models.
Article
Computer Science, Artificial Intelligence
Irena Spasic et al.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2020)
Article
Computer Science, Artificial Intelligence
Liang-Chih Yu et al.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2020)
Article
Computer Science, Artificial Intelligence
Syed Muhammad Ali et al.
JOURNAL OF INTELLIGENT INFORMATION SYSTEMS
(2020)
Article
Computer Science, Software Engineering
Abu Naser Masud et al.
JOURNAL OF SYSTEMS AND SOFTWARE
(2020)
Proceedings Paper
Automation & Control Systems
Haozhou Li et al.
2020 CHINESE AUTOMATION CONGRESS (CAC 2020)
(2020)
Article
Computer Science, Information Systems
Faliang Huang et al.
Article
Computer Science, Artificial Intelligence
P. Kalarani et al.
Article
Computer Science, Artificial Intelligence
Asad Abdi et al.
Proceedings Paper
Computer Science, Artificial Intelligence
Shrabanti Mandal et al.
COMPUTING, COMMUNICATION AND SIGNAL PROCESSING, ICCASP 2018
(2019)
Article
Information Science & Library Science
Byeongki Jeong et al.
INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT
(2019)
Review
Computer Science, Artificial Intelligence
Lu-yu Dong et al.
EXPERT SYSTEMS WITH APPLICATIONS
(2018)
Article
Computer Science, Artificial Intelligence
Xianghua Fu et al.
KNOWLEDGE-BASED SYSTEMS
(2018)
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Shufeng Xiong et al.
Article
Computer Science, Artificial Intelligence
Zongda Wu et al.
EXPERT SYSTEMS WITH APPLICATIONS
(2017)
Article
Computer Science, Artificial Intelligence
Faliang Huang et al.
Proceedings Paper
Computer Science, Artificial Intelligence
Nadeem Akhtar et al.
7TH INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING & COMMUNICATIONS (ICACC-2017)
(2017)
Proceedings Paper
Computer Science, Information Systems
Bo Xu et al.
SOCIAL MEDIA PROCESSING, SMP 2016
(2016)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Mohamed Dermouche et al.
30TH ANNUAL ACM SYMPOSIUM ON APPLIED COMPUTING, VOLS I AND II
(2015)
Article
Computer Science, Artificial Intelligence
Rodrigo Moraes et al.
EXPERT SYSTEMS WITH APPLICATIONS
(2013)
Article
Computer Science, Artificial Intelligence
Chenghua Lin et al.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2012)
