Feature distillation network for aspect-based sentiment analysis

Shuang, Kai; Yang, Qianqian; Loo, Jonathan; Li, Rui; Gu, Mengyu

doi:10.1016/j.inffus.2020.03.003

Cited by 41 publications

(14 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To address the limitations in RNN such as lacking position invariance and lacking sensitivity to local key patterns, Liu and Shen [13] proposed a Gated Alternate Neural Network(GANN), which designs a special module named the Gate Truncation RNN (GTR) to learn informative aspectdependent sentiment clue representations. Shuang et al [14] proposed a feature distillation network (FDN) for reducing noise and distilling aspect-relevant sentiment features. To address the problem of insufficient aspect representation learning, Jiang et al [15] proposed a mutually enhanced transformation network (METNet) for the ABSA task.…”

Section: B Methods Based On Recurrent Neural Networkmentioning

confidence: 99%

A Local and Global Context Focus Multilingual Learning Model for Aspect-Based Sentiment Analysis

Wumaier

Kadeer

et al. 2022

IEEE Access

View full text Add to dashboard Cite

Aspect-Based Sentiment Analysis (ABSA) aims to predict the sentiment polarity of different aspects in a sentence or document, which is a fine-grained task of natural language processing. Most of the existing work focuses on the correlation between aspect sentiment polarity and local context. The important deep correlations between global context and aspect sentiment polarity have not received enough attention. Besides, there are few studies on Chinese ABSA tasks and multilingual ABSA tasks. Based on the local context focus mechanism, we propose a multilingual learning model based on the interactive learning of local and global context focus, namely LGCF. Compared with the existing models, this model can effectively learn the correlation between local context and aspect words and the correlation between global context and aspect words simultaneously. In addition, the model can effectively analyze comments in Chinese and English simultaneously. Experiments conducted on three Chinese benchmark datasets(Camera, Phone and Car) and six English benchmark datasets(Laptop, Restaurant14, Restaurant16, Twitter, Tshirt and Television) demonstrate that LGCF has achieved compelling performance and efficiency improvements compared with several existing state-of-the-art models. Moreover, the ablation experiment results also verify the effectiveness of each part in LGCF. INDEX TERMS aspect-based sentiment analysis; Chinese sentiment analysis; multilingual ABSA; Local and Global Context Focus

show abstract

Section: B Methods Based On Recurrent Neural Networkmentioning

confidence: 99%

A Local and Global Context Focus Multilingual Learning Model for Aspect-Based Sentiment Analysis

Wumaier

Kadeer

et al. 2022

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Their experiments demonstrated that both an increase in embedding dimensionality and an increase in the volume of health‐related training data could improve classification accuracy. As opposed to this finding, a comparative study with the conventional BoW model showed that in many cases the BoW model is superior to the word embeddings, particularly in the application of sentiment analysis (Blair et al, 2020; Shuang et al, 2020).…”

Section: Related Workmentioning

confidence: 96%

Sentiment classification in social media data by combining triplet belief functions

2021

Asso for Info Science & Tech

View full text Add to dashboard Cite

Sentiment analysis is an emerging technique that caters for semantic orientation and opinion mining. It is increasingly used to analyse online reviews and posts for identifying people's opinions and attitudes to products and events in order to improve business performance of companies and aid to make better organising strategies of events. This paper presents an innovative approach to combining the outputs of sentiment classifiers under the framework of belief functions. It consists of the formulation of sentiment classifier outputs in the triplet evidence structure and the development of general formulas for combining triplet functions derived from sentiment classification results via three evidential combination rules along with comparative analyses. The empirical studies have been conducted on examining the effectiveness of our method for sentiment classification individually and in combination, and the results demonstrate that the best combined classifiers by our method outperforms the best individual classifiers over five review datasets.

show abstract

“…According to unimodal affect recognition based on the text [126,402], audio [178,194,199], visual [253,281,403], EEG [404], or ECG [336], we can find that the most widely used modality is the visual signal, mainly consisting of facial expressions and body gestures. The number of visual-based emotion recognition systems is comparable to the sum of that of systems based on other modalities since the visual signals are easier to capture than other signals and emotional information in visual signals is more helpful than other signals in recognizing the emotion state of human beings.…”

Section: Effects Of Different Signals On Unimodal Affect Recognitionmentioning

confidence: 99%

A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances

Wang¹,

Song²,

Tao³

et al. 2022

Preprint

View full text Add to dashboard Cite

Affective computing conjoins the research topics of emotion recognition and sentiment analysis, and can be realized with unimodal or multimodal data, consisting primarily of physical information (e.g., text, audio, and visual) and physiological signals (e.g., EEG and ECG). Physical-based affect recognition caters to more researchers due to the availability of multiple public databases, but it is challenging to reveal one's inner emotion hidden purposefully from facial expressions, audio tones, body gestures, etc. Physiological signals can generate more precise and reliable emotional results; yet, the difficulty in acquiring these signals hinders their practical application. Besides, by fusing physical information and physiological signals, useful features of emotional states can be obtained to enhance the performance of affective computing models. While existing reviews focus on one specific aspect of affective computing, we provide a systematical survey of important components: emotion models, databases, and recent advances. Firstly, we introduce two typical emotion models followed by five kinds of commonly used databases for affective computing. Next, we survey and taxonomize state-of-the-art unimodal affect recognition and multimodal affective analysis in terms of their detailed architectures and performances. Finally, we discuss some critical aspects of affective computing and its applications and conclude this review by pointing out some of the most promising future directions, such as the establishment of benchmark database and fusion strategies. The overarching goal of this systematic review is to help academic and industrial researchers understand the recent advances as well as new developments in this fast-paced, high-impact domain.

show abstract

Feature distillation network for aspect-based sentiment analysis

Cited by 41 publications

References 18 publications

A Local and Global Context Focus Multilingual Learning Model for Aspect-Based Sentiment Analysis

A Local and Global Context Focus Multilingual Learning Model for Aspect-Based Sentiment Analysis

Sentiment classification in social media data by combining triplet belief functions

A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances

Contact Info

Product

Resources

About