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Article
Computer Science, Artificial Intelligence
Bo Huang et al.
Summary: Attention-based models are widely used in aspect-level sentiment analysis due to their contextual semantic-alignment capabilities. However, these models lack the ability to incorporate syntactic tendencies, resulting in lower accuracy when dealing with complex syntactic relationships and long-span syntactically dependent utterances. To address this problem, a neural network model combining a conditional random field and a graph convolutional network is proposed. The proposed model integrates contextual information within the opinion span to global nodes and predicts aspect-specific sentiment polarity labels by computing vector expressions of the global nodes.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Chockalingam Arumugam et al.
Summary: This study proposes two novel approaches for improving the effectiveness of sentiment classification. The first approach incorporates adaptive weights into Aspect-Specific GCN (AASGCN) to better capture the semantic meaning of the opinion target. The second approach introduces emotional intensive information into sentiment reasoning. Experimental results show that AASGCN outperforms state-of-the-art models and can be substantially improved by incorporating these two approaches.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Theory & Methods
Tariq Abdullah et al.
Summary: Humans are integrated with devices that collect vast unstructured opinionated data, and sentiment analysis is crucial for accurately analyzing subjective information. Deep learning, specifically Transformer language models, has become the dominant approach for sentiment analysis, surpassing other machine learning methods. This survey discusses recent trends in architectures, focusing on theory, design, and implementation, and provides an overview of state-of-the-art Transformer-based language models' performance on benchmark datasets. The survey also addresses open challenges in NLP and sentiment analysis.
ACM COMPUTING SURVEYS
(2023)
Article
Computer Science, Artificial Intelligence
Ankita Gandhi et al.
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
Huyen Trang Phan et al.
Summary: Aspect-level sentiment analysis (ALSA) is crucial in social networks, especially in e-commerce. This study provides a comprehensive survey on GCN-based ALSA methods, proposing a novel taxonomy and discussing benchmark datasets and text representations commonly used. The study also highlights future research directions and challenges in this field.
INFORMATION FUSION
(2023)
Article
Computer Science, Artificial Intelligence
Tiquan Gu et al.
Summary: This paper proposes a graph convolutional network that fuses external knowledge for aspect-based sentiment analysis. The experimental results demonstrate the effectiveness of fully integrating external knowledge in completing the task.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Bin Liang et al.
Summary: This paper proposes a graph convolutional network model Sentic GCN based on SenticNet to enhance the affective dependencies of sentences for aspect-based sentiment analysis. By integrating emotional knowledge from SenticNet, the model effectively handles contextual affective information in sentences, improving the effectiveness of sentiment polarity detection towards specific aspects.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Haiyan Wu et al.
Summary: Aspect-based Sentiment Analysis (ABSA) is a method that aims to identify the sentiment polarity of specific aspects in a sentence. This paper proposes a phrase dependency graph attention network (PD-RGAT) based on a relational graph constructed from the phrase dependency graph, and experimental results demonstrate its effectiveness.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Luwei Xiao et al.
Summary: This paper proposes a novel GNN based deep learning model for aspect-based sentiment classification, which effectively utilizes syntactic structures and semantic dependencies between contextual words to achieve state-of-the-art performance.
Article
Business
Amit Singh et al.
Summary: This paper proposes a text-analytics framework integrating aspect-level sentiment analysis with bias-corrected least square dummy variable method to examine the influence of review-embedded information on product sales empirically. The findings suggest that review volume and the sentiments corresponding to the exterior significantly influence mid-size car sales in India.
JOURNAL OF BUSINESS RESEARCH
(2022)
Article
Computer Science, Information Systems
Huyen Trang Phan et al.
Summary: This study proposes a new method called the CANN-SSCG model to address the limitations of graph convolutional network (GCN)-based aspect-level sentiment analysis methods. The method constructs three different heterogeneous graphs and combines them into a general heterogeneous graph. Finally, a convolutional neural network algorithm is used for aspect-level sentiment analysis, and promising results are achieved.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Interdisciplinary Applications
Yan Xiao et al.
Summary: The article introduces a new approach to fine-grained sentiment analysis of Chinese consumer data in the context of Lean manufacturing. The new approach extracts customer value from online reviews and guides Lean improvement. Experimental results show that the new approach outperforms traditional methods, and a use case demonstrates its practical importance.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Computer Science, Information Systems
Meng Zhao et al.
Summary: This paper proposes an aggregated graph convolutional network (AGCN) to enhance the representation ability of target nodes in aspect-based sentiment analysis. The AGCN updates the node representation iteratively using aggregator functions, and uses subdependency and attention mechanism to extract and capture sentiment dependencies between node feature information. Experimental results show that AGCN is effective compared to other GCN-based methods.
INFORMATION SCIENCES
(2022)
Article
Transportation
Tony Diana
JOURNAL OF AIR TRANSPORT MANAGEMENT
(2022)
Article
Business
Yan Xiao et al.
Summary: This paper proposes fine-grained sentiment analysis for preference mining and utilizes deep neural networks to improve the performance. Experimental results on a user review data set demonstrate that the proposed approach outperforms baseline models.
JOURNAL OF RETAILING AND CONSUMER SERVICES
(2022)
Article
Computer Science, Artificial Intelligence
Qiang Lu et al.
Summary: This study introduces an aspect-gated graph convolutional network (AGGCN) with a special aspect gate designed to guide the encoding of aspect-specific information from the beginning. It constructs a graph convolution network on the sentence dependency tree to fully utilize syntactical information and sentiment dependencies, outperforming strong baseline models in sentiment analysis tasks.
APPLIED INTELLIGENCE
(2021)
Article
Computer Science, Artificial Intelligence
Liangqi Cai et al.
Summary: This study proposes a two-stage coarse-to-fine POI recommendation algorithm based on tensor factorization and weighted distance kernel density estimation. The method takes into account long-term preferences and crowd preferences to estimate user interests, and considers spatial distance to determine fine-grained user-location interests.
Article
Computer Science, Information Systems
Yijiang Liu et al.
Summary: This paper focuses on the extraction of opinion target-word pairs from user reviews, proposing a new task named Aspect-Based Pair-wise Opinion Generation (ABPOG) and developing a new model to address this issue. Experimental results demonstrate the effectiveness of the model on a Chinese automotive reviews dataset.
INFORMATION PROCESSING & MANAGEMENT
(2021)
Article
Automation & Control Systems
Sunghong Park et al.
Summary: Customers' evaluations on products can be derived through machine learning by analyzing online reviews. The proposed customer sentiment analysis method works sensibly by expanding sentiment words to include non-standard expressions, leading to a more detailed evaluation pattern. The method also includes a design of an index for customer dissatisfaction, combining 'controversy' and 'complaint' to indicate both coverage and degree of dissatisfaction.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2021)
Article
Computer Science, Artificial Intelligence
Chao Wu et al.
Summary: Aspect-Based Sentiment Analysis (ABSA) is a fine-grained sentiment analysis task that aims to detect target-aspect-sentiment elements in sentences. The proposed end-to-end multiple-element joint detection model (MEJD) effectively extracts all (target, aspect, sentiment) triples from sentences and achieves state-of-the-art performance in sentiment extraction.
KNOWLEDGE-BASED SYSTEMS
(2021)
Review
Computer Science, Artificial Intelligence
Zhaoyang Niu et al.
Summary: This paper provides an overview of state-of-the-art attention models and defines a unified model suitable for most attention structures. It describes in detail each step of the attention mechanism implemented in the model and classifies existing attention models based on four criteria. Additionally, it summarizes the use of attention mechanisms in network architectures and typical applications.
Article
Business
Xinxin Ren et al.
Summary: A two-stage hybrid model is proposed in this study, which effectively analyzes the usage characteristics of e-coupons by consumer segmentation and model construction for different consumer segments, improving prediction performance and consumer satisfaction.
JOURNAL OF RETAILING AND CONSUMER SERVICES
(2021)
Article
Computer Science, Artificial Intelligence
Yang Liu et al.
NEURAL COMPUTING & APPLICATIONS
(2020)
Article
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Haoyue Liu et al.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2020)
Review
Hospitality, Leisure, Sport & Tourism
Hengyun Li et al.
INTERNATIONAL JOURNAL OF CONTEMPORARY HOSPITALITY MANAGEMENT
(2020)
Review
Computer Science, Artificial Intelligence
Hai Ha Do et al.
EXPERT SYSTEMS WITH APPLICATIONS
(2019)
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Artem Timoshenko et al.
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Muhammad Hassan Arif et al.
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Amy J. C. Trappey et al.
ADVANCED ENGINEERING INFORMATICS
(2018)
Review
Computer Science, Artificial Intelligence
Lei Zhang et al.
WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY
(2018)
Review
Environmental Studies
Huimin Tan et al.
TOURISM MANAGEMENT
(2018)
Article
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Feng Zhou et al.
EXPERT SYSTEMS WITH APPLICATIONS
(2017)
Article
Business
Zhi-Ping Fan et al.
JOURNAL OF BUSINESS RESEARCH
(2017)
Review
Computer Science, Artificial Intelligence
Shankhadeep Banerjee et al.
DECISION SUPPORT SYSTEMS
(2017)
Review
Automation & Control Systems
Jian Jin et al.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2016)
Article
Engineering, Mechanical
Feng Zhou et al.
JOURNAL OF MECHANICAL DESIGN
(2015)
Article
Computer Science, Artificial Intelligence
Andrej Gisbrecht et al.
Article
Engineering, Multidisciplinary
Walaa Medhat et al.
AIN SHAMS ENGINEERING JOURNAL
(2014)
Article
Computer Science, Artificial Intelligence
Qiang Ye et al.
EXPERT SYSTEMS WITH APPLICATIONS
(2009)
