Aspect-Based Sentiment Analysis: A Survey of Deep Learning Methods

Liu, Haoyue; Chatterjee, Ishani; Zhou, MengChu; Lu, Xiaoyu Sean; Abusorrah, Abdullah

doi:10.1109/tcss.2020.3033302

Cited by 119 publications

(47 citation statements)

References 61 publications

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“…Following some pioneering works, a wide variety of frameworks have been proposed to tackle different compound ABSA tasks for enabling aspect-level opinion mining in different scenarios. However, a systematic review of various ABSA tasks, especially recent progress on compound ABSA tasks, is lacking in the existing surveys [1,5,14,6,15,16], which we aim to fill through this survey.…”

Section: Sentiment Polarity Posmentioning

confidence: 99%

A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges

Zhang¹,

Li²,

Yang³

et al. 2022

Preprint

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As an important fine-grained sentiment analysis problem, aspect-based sentiment analysis (ABSA), aiming to analyze and understand people's opinions at the aspect level, has been attracting considerable interest in the last decade. To handle ABSA in different scenarios, various tasks have been introduced for analyzing different sentiment elements and their relations, including the aspect term, aspect category, opinion term, and sentiment polarity. Unlike early ABSA works focusing on a single sentiment element, many compound ABSA tasks involving multiple elements have been studied in recent years for capturing more complete aspect-level sentiment information. However, a systematic review of various ABSA tasks and their corresponding solutions is still lacking, which we aim to fill in this survey. More specifically, we provide a new taxonomy for ABSA which organizes existing studies from the axes of concerned sentiment elements, with an emphasis on recent advances of compound ABSA tasks. From the perspective of solutions, we summarize the utilization of pre-trained language models for ABSA, which improved the performance of ABSA to a new stage. Besides, techniques for building more practical ABSA systems in cross-domain/lingual scenarios are discussed. Finally, we review some emerging topics and discuss some open challenges to outlook potential future directions of ABSA.

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Section: Sentiment Polarity Posmentioning

confidence: 99%

A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges

Zhang¹,

Li²,

Yang³

et al. 2022

Preprint

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“…It is very inconvenient that different unikernels need to build and generate a matching tool chain, and configure the corresponding development environment. So we can build comprehensive and easy-to-use tools for quickly compiling application into Unikernel, like Unik [86] to facilitate more applications, e.g., [87][88][89][90][91][92][93][94][95][96][97].…”

Section: )mentioning

confidence: 99%

Evolving Container to Unikernel for Edge Computing and Applications in Process Industry

Chen

Zhou

2021

Processes

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Industry 4.0 promotes manufacturing and process industry towards digitalization and intellectualization. Edge computing can provide delay-sensitive services in industrial processes to realize intelligent production. Lightweight virtualization technology is one of the key elements of edge computing, which can implement resource management, orchestration, and isolation services without considering heterogenous hardware. It has revolutionized software development and deployment. The scope of this review paper is to present an in-depth analysis of two such technologies, Container and Unikernel, for edge computing. We discuss and compare their applicability in terms of migration, security, and orchestration for edge computing and industrial applications. We describe their performance indexes, evaluation methods and related findings. We then discuss their applications in industrial processes. To promote further research, we present some open issues and challenges to serve as a road map for both researchers and practitioners in the areas of Industry 4.0, industrial process automation, and advanced computing.

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“…In [ 18 ], Zhang et al proposed a knowledge-guided capsule network to address the above limitations using Bi-LSTM and capsule attention network. The study in [ 19 ] summarizes the state-of-the-art ABSA methods by using lexicon-based, machine learning, and deep learning approaches.…”

Section: Related Workmentioning

confidence: 99%

“…The performance of various sentiment analysis methods differs due to such factors as datasets, feature representations, or classification processes. Liu et al [ 19 ] conducted a detailed survey on several deep learning approaches for aspect-based sentiment analysis using benchmark datasets evaluation metrics and the performance of the existing deep learning methods.…”

Section: Related Workmentioning

confidence: 99%

Statistics-Based Outlier Detection and Correction Method for Amazon Customer Reviews

Chatterjee

Zhou

Abusorrah

et al. 2021

Entropy

Self Cite

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People nowadays use the internet to project their assessments, impressions, ideas, and observations about various subjects or products on numerous social networking sites. These sites serve as a great source to gather data for data analytics, sentiment analysis, natural language processing, etc. Conventionally, the true sentiment of a customer review matches its corresponding star rating. There are exceptions when the star rating of a review is opposite to its true nature. These are labeled as the outliers in a dataset in this work. The state-of-the-art methods for anomaly detection involve manual searching, predefined rules, or traditional machine learning techniques to detect such instances. This paper conducts a sentiment analysis and outlier detection case study for Amazon customer reviews, and it proposes a statistics-based outlier detection and correction method (SODCM), which helps identify such reviews and rectify their star ratings to enhance the performance of a sentiment analysis algorithm without any data loss. This paper focuses on performing SODCM in datasets containing customer reviews of various products, which are (a) scraped from Amazon.com and (b) publicly available. The paper also studies the dataset and concludes the effect of SODCM on the performance of a sentiment analysis algorithm. The results exhibit that SODCM achieves higher accuracy and recall percentage than other state-of-the-art anomaly detection algorithms.

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Aspect-Based Sentiment Analysis: A Survey of Deep Learning Methods

Cited by 119 publications

References 61 publications

A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges

A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges

Evolving Container to Unikernel for Edge Computing and Applications in Process Industry

Statistics-Based Outlier Detection and Correction Method for Amazon Customer Reviews

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