Multi-view and Attention-Based BI-LSTM for Weibo Emotion Recognition

Cai, Xiaofeng; Hao, Zhifeng

doi:10.2991/ncce-18.2018.127

Cited by 5 publications

(2 citation statements)

References 2 publications

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“…They further compared and discussed techniques that attained greater F1 measure and realized that such systems were implemented using Bi‐LSTMs. Cai and Hao 78 in their work tried to detect emotions from Weibo, a popular Chinese microblogging site, using multiview and attention‐based Bi‐LSTM. The authors approached the problem in three levels by finding emotions from emotion words, emojis, and extracted semantic or hidden emotion expressions.…”

Section: Detection Approaches and Related Workmentioning

confidence: 99%

Text‐based emotion detection: Advances, challenges, and opportunities

Acheampong

Chen

Nunoo‐Mensah

2020

Engineering Reports

220

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Emotion detection (ED) is a branch of sentiment analysis that deals with the extraction and analysis of emotions. The evolution of Web 2.0 has put text mining and analysis at the frontiers of organizational success. It helps service providers provide tailor-made services to their customers. Numerous studies are being carried out in the area of text mining and analysis due to the ease in sourcing for data and the vast benefits its deliverable offers. This article surveys the concept of ED from texts and highlights the main approaches adopted by researchers in the design of text-based ED systems. The article further discusses some recent state-of-the-art proposals in the field. The proposals are discussed in relation to their major contributions, approaches employed, datasets used, results obtained, strengths, and their weaknesses. Also, emotion-labeled data sources are presented to provide neophytes with eligible text datasets for ED. Finally, the article presents some open issues and future research direction for text-based ED. K E Y W O R D S emotion detection, natural language processing, sentiment analysis, text-based emotion detection This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Detection Approaches and Related Workmentioning

confidence: 99%

Text‐based emotion detection: Advances, challenges, and opportunities

Acheampong

Chen

Nunoo‐Mensah

2020

Engineering Reports

220

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“…In reference [38], multichannel was the use of CNN and LSTM to generate information channels for Vietnamese sentiment analysis. In multiview, Cai and Hao [39] proposed a multiview and attention-based BI-LSTM method for Weibo emotion recognition. Huang et al [40].…”

Section: Related Workmentioning

confidence: 99%

Variable Convolution and Pooling Convolutional Neural Network for Text Sentiment Classification

Dong

Tang

et al. 2020

IEEE Access

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With the popularity of the internet, the expression of emotions and methods of communication are becoming increasingly abundant, and most of these emotions are transmitted in text form. Text sentiment classification research mainly includes three methods based on sentiment dictionaries, machine learning and deep learning. In recent years, many deep learning-based works have used TextCNN (text convolution neural network) to extract text semantic information for text sentiment analysis. However, TextCNN only considers the length of the sentence when extracting semantic information. It ignores the semantic features between word vectors and only considers the maximum feature value of the feature image in the pooling layer without considering other information. Therefore, in this paper, we propose a convolutional neural network based on multiple convolutions and pooling for text sentiment classification (variable convolution and pooling convolution neural network, VCPCNN). There are three contributions in this paper. First, a multiconvolution and pooling neural network is proposed for the TextCNN network structure. Second, four convolution operations are introduced in the word embedding dimension or direction, which are helpful for mining the local features on the semantic dimensions of word vectors. Finally, average pooling is introduced in the pooling layer, which is beneficial for saving the important feature information of the extracted features. The verification test was carried out on four emotional datasets, including English emotional polarity, Chinese emotional polarity, Chinese subjective and objective emotion and Chinese multicategory. Our apporach is effective in that its result was up to 1.97% higher than that of the TextCNN network. INDEX TERMS Text sentiment classification, deep learning, CNN.

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A Survey on Sentiment Analysis in Health Care: New Opportunities and Challenges

Kumar

Shekhar

2023

Advances in Intelligent Systems and Computing

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Multi-view and Attention-Based BI-LSTM for Weibo Emotion Recognition

Cited by 5 publications

References 2 publications

Text‐based emotion detection: Advances, challenges, and opportunities

Text‐based emotion detection: Advances, challenges, and opportunities

Variable Convolution and Pooling Convolutional Neural Network for Text Sentiment Classification

A Survey on Sentiment Analysis in Health Care: New Opportunities and Challenges

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