Results 121 to 130 of about 265,995 (181)

Analyzing Sleep Behavior Using BERT-BiLSTM and Fine-Tuned GPT-2 Sentiment Classification: Comparison Study.

JMIR Med Inform
Deng Y, van der Meer J, Tzovara A, Schmidt M, Bassetti C, Denecke K.   +5 more
europepmc   +1 more source

Interactive Dual Attention Network for Text Sentiment Classification. [PDF]

Comput Intell Neurosci, 2020
Zhu Y, Zheng W, Tang H.
europepmc   +1 more source

Improving Document-Level Sentiment Classification Using Importance of Sentences. [PDF]

Entropy (Basel), 2020
Choi G, Oh S, Kim H.
europepmc   +1 more source

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Sentiment Lexicon Enhanced Neural Sentiment Classification

Proceedings of the 28th ACM International Conference on Information and Knowledge Management, 2019
Sentiment classification is an important task in the sentiment analysis field. Many deep learning based sentiment classification methods have been proposed in recent years. However, these methods usually rely on massive labeled texts to train sentiment classifiers, which are expensive and time-consuming to annotate. Luckily, many high-quality sentiment
Chuhan Wu, Fangzhao Wu, Junxin Liu, Yongfeng Huang, Xing Xie   +4 more
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Imbalanced sentiment classification

Proceedings of the 20th ACM international conference on Information and knowledge management, 2011
Sentiment classification has undergone significant development in recent years. However, most existing studies assume the balance between negative and positive samples, which may not be true in reality. In this paper, we investigate imbalanced sentiment classification instead.
Shoushan Li, Guodong Zhou, Zhongqing Wang, Sophia Yat Mei Lee, Rangyang Wang   +4 more
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Fuzzy Sentiment Membership Determining for Sentiment Classification

2014 IEEE International Conference on Data Mining Workshop, 2014
Traditional support vector machine treats all samples using the same weight. Therefore it is very sensitive to noisy data. While the fuzzy support vector machine assigns lower weights to the samples which make small contributions to classification, thus it is beneficial to reduce the effects of noisy and unimportant data on the classification accuracy ...
Chuanjun Zhao, Suge Wang, Deyu Li
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Microblog sentiment classification with heterogeneous sentiment knowledge

Information Sciences, 2016
Microblogging services, such as Twitter, are very popular for information release and dissemination. Analyzing the sentiments in massive microblog messages is useful for sensing the public's opinions on various topics, which has wide applications in both academic and industrial fields.
Wu, Fangzhao, Song, Yangqiu, Huang, Yongfeng   +2 more
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Context-aware sentiment classification

2015 Fifteenth International Conference on Advances in ICT for Emerging Regions (ICTer), 2015
Sentiment Analysis is a growing research area of Natural Language Processing which aims at identifying positive and negative opinions and emotions from a textual data. Presently, Sentiment Analysis research works range from document level classification to sentence-level, phrase-level or aspect/feature level analysis.
Buddhika H. Kasthuriarachchy, Kasun de Zoysa, H.L. Premarathne   +2 more
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