By Tru Cao, Ee-Peng Lim, Zhi-Hua Zhou, Tu-Bao Ho, David Cheung, Hiroshi Motoda
This two-volume set, LNAI 9077 + 9078, constitutes the refereed court cases of the nineteenth Pacific-Asia convention on Advances in wisdom Discovery and information Mining, PAKDD 2015, held in Ho Chi Minh urban, Vietnam, in could 2015.
The lawsuits comprise 117 paper rigorously reviewed and chosen from 405 submissions. they've been geared up in topical sections named: social networks and social media; category; computing device studying; functions; novel tools and algorithms; opinion mining and sentiment research; clustering; outlier and anomaly detection; mining doubtful and vague information; mining temporal and spatial info; characteristic extraction and choice; mining heterogeneous, high-dimensional, and sequential info; entity solution and topic-modeling; itemset and high-performance information mining; and recommendations.
Read or Download Advances in Knowledge Discovery and Data Mining: 19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part II PDF
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Additional resources for Advances in Knowledge Discovery and Data Mining: 19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part II
The rest of the paper is organized as follows: In Section 2, we introduce some related work including word representations, recursive deep learning and Stanford Sentiment Treebank. Section 3 introduces our Parallel Recursive Deep Model and describes the details of parameter learning. Section 4 presents the experiments on predicting sentiment label distributions on both sentences and phrases level. The conclusion and future work are presented in Section 5. All the datasets, code and all relevant parameter speciﬁcations are publicly available2 .
Constructing the aﬀective lexicon ontology. Journal of the China Society for Scientiﬁc and Technical Information 27(2), 180–185 (2008) 8. : Eﬃcient estimation of word representations in vector space. In: 1st ICLR (2013) 9. : Emotion tokens: bridging the gap among multilingual twitter sentiment analysis. , Khelalfa, H. ) AIRS 2011. LNCS, vol. 7097, pp. 238–249. Springer, Heidelberg (2011) 10. : A weakly supervised approach to chinese sentiment classiﬁcation using partitioned self-training. Journal of Information Science 39(6), 815–831 (2013) 11.
Zhijin Wang, Yan Yang, Qinmin Hu, and Liang He 747 Author Index . . . . . . . . . . . . . . . . . . . . . . com Abstract. Micro-blog emotion mining and emotion cause extraction are essential in social network data mining. This paper presents a novel approach on Chinese micro-blog emotion cause detection based on the ECOCC model, focusing on mining factors for eliciting some kinds of emotions. In order to do so, the corresponding emotion causes are extracted.
Advances in Knowledge Discovery and Data Mining: 19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part II by Tru Cao, Ee-Peng Lim, Zhi-Hua Zhou, Tu-Bao Ho, David Cheung, Hiroshi Motoda