Improving aspect-based sentiment analysis via aligning aspect embedding

Tan, Xingwei; Cai, Yi; Xu, Jingyun; Leung, Ho-fung; Chen, Wen Hao; Li, Qing

doi:10.1016/j.neucom.2019.12.035

Cited by 37 publications

(10 citation statements)

References 16 publications

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“…In recent years, the aspect level has become more and more popular, and with the application of deep learning technology, it has become better at capturing the semantic relationship between aspect terms and words in a more quantifiable way (Huang et al 2018). The process of sentiment analysis involves the coordination of multiple tasks, and the subtasks include feature extraction (Bouktif et al 2020;Lin et al 2020), context analysis (Yu et al 2019;Zuo et al 2020), and the application of some analytical models (Tan et al 2020).…”

Section: Analysis On Research Methods and Topics Of The C3 Communitymentioning

confidence: 99%

Survey on sentiment analysis: evolution of research methods and topics

Cui

Wang

et al. 2023

Artif Intell Rev

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Sentiment analysis, one of the research hotspots in the natural language processing field, has attracted the attention of researchers, and research papers on the field are increasingly published. Many literature reviews on sentiment analysis involving techniques, methods, and applications have been produced using different survey methodologies and tools, but there has not been a survey dedicated to the evolution of research methods and topics of sentiment analysis. There have also been few survey works leveraging keyword cooccurrence on sentiment analysis. Therefore, this study presents a survey of sentiment analysis focusing on the evolution of research methods and topics. It incorporates keyword co-occurrence analysis with a community detection algorithm. This survey not only compares and analyzes the connections between research methods and topics over the past two decades but also uncovers the hotspots and trends over time, thus providing guidance for researchers. Furthermore, this paper presents broad practical insights into the methods and topics of sentiment analysis, while also identifying technical directions, limitations, and future work.

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Section: Analysis On Research Methods and Topics Of The C3 Communitymentioning

confidence: 99%

Survey on sentiment analysis: evolution of research methods and topics

Cui

Wang

et al. 2023

Artif Intell Rev

View full text Add to dashboard Cite

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“…Pejic-Bach, Bertoncel, Meško, and Krstić (2020) used the TM technique for investigating job advertisements in industry 4.0. Tan et al (2020) presented a learning method according to the aspect categories and the aspect terms relation can learn aspect embedding. The objective is to address the relation between the two subtasks of aspect-based sentiment analysis(ABSA): aspect category sentiment analysis (ACSA) and aspect term sentiment analysis (ATSA).…”

Section: Text Mining and Sentiment Analysismentioning

confidence: 99%

Introducing a novel revenue-sharing contract in media supply chain management using data mining and multi-criteria decision-making methods

2022

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In recent years, we have observed stunning advances in supply chain coordination. But despite all these scientific methods, some of the businesses are still coordinating traditionally. Media supply chain, and above all its components, the cinema industry has been coordinated traditionally for years. One of the boldest problems in the media supply chain is wage determination in the cinema industry, which in it the actor's efficiency or deficiency is not considered. It means the success or failure of the movie is nothing to do with the actor's revenue. To address this problem, this article proposes a novel revenue-sharing coordination contract that benefits from text mining and the best-worst method. We aim to determine a fair share of profit or loss of a movie for each of its actors based on their performance and efficiency on the success or failure of the project. One of the latest movies is considered as a case study, and its lead actor's wage for this movie is determined under this contract. The sentiment analysis technique investigated the viewers' opinions about the movie and the actor's performance. Since the reviewers mainly are the people who have no expertise in cinematography, they cannot discern the influence of other participants on the actor's performance. So the proposed method used experts' opinions through the fuzzy DEMATEL method to consider the overlapping effect besides enhancing the validity of the reviewers' opinions.

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“…Jiang et al ( 2011 ) took the content, sentiment lexicon and context into consideration to improve the target-dependent sentiment classification for Twitter. Tan et al ( 2020 ) proposed an aligning aspect embedding method to train aspect embeddings for the ASC. The embeddings are applied to the gated convolutional neural networks (CNNs) and attention-based LSTM.…”

Section: Related Workmentioning

confidence: 99%

Position-Enhanced Multi-Head Self-Attention Based Bidirectional Gated Recurrent Unit for Aspect-Level Sentiment Classification

Ding

et al. 2022

Front. Psychol.

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Aspect-level sentiment classification (ASC) is an interesting and challenging research task to identify the sentiment polarities of aspect words in sentences. Previous attention-based methods rarely consider the position information of aspect and contextual words. For an aspect word in a sentence, its adjacent words should be given more attention than the long distant words. Based on this consideration, this article designs a position influence vector to represent the position information between an aspect word and the context. By combining the position influence vector, multi-head self-attention mechanism and bidirectional gated recurrent unit (BiGRU), a position-enhanced multi-head self-attention network based BiGRU (PMHSAT-BiGRU) model is proposed. To verify the effectiveness of the proposed model, this article makes a large number of experiments on SemEval2014 restaurant, SemEval2014 laptop, SemEval2015 restaurant, and SemEval2016 restaurant data sets. The experiment results show that the performance of the proposed PMHSAT-BiGRU model is obviously better than the baselines. Specially, compared with the original LSTM model, the Accuracy values of the proposed PMHSAT-BiGRU model on the four data sets are improved by 5.72, 6.06, 4.52, and 3.15%, respectively.

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Improving aspect-based sentiment analysis via aligning aspect embedding

Cited by 37 publications

References 16 publications

Survey on sentiment analysis: evolution of research methods and topics

Survey on sentiment analysis: evolution of research methods and topics

Introducing a novel revenue-sharing contract in media supply chain management using data mining and multi-criteria decision-making methods

Position-Enhanced Multi-Head Self-Attention Based Bidirectional Gated Recurrent Unit for Aspect-Level Sentiment Classification

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