@inproceedings{d3dc2e3a1c23420dade97c04b2512e97,
title = "A Transfer Learning Based Boosting Model for Emotion Analysis",
abstract = "Emotion Analysis determines the emotion of a text. Supervised Machine learning algorithms are effective for Emotion Analysis, but they need a lot of labelled data. It is a labor-intensive process and often needs instructions of experts to annotate data. In this paper, we propose a transfer learning approach for emotion analysis based on Adaboost(EATAdaBoost) by adapting the knowledge learned from labelled source data to the target domain which has none or few labelled data. We try to establish connections between source instances and target domain. Word2vec semantic similarities between source instances and common non-domain-specific emotional words which occur frequently in both domains are used as a bridge. If the similarity is bigger than a threshold, we think the source instance is useful for learning target task. In addition, we conduct extensive experiments and the results show that our algorithm is superior to baselines.",
keywords = "AdaBoost, Emotion Analysis, Transfer Learning",
author = "Ruolan Yong and Chengyu Wang and Xiaofeng He",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 8th IEEE International Conference on Big Knowledge, ICBK 2017 ; Conference date: 09-08-2017 Through 10-08-2017",
year = "2017",
month = aug,
day = "30",
doi = "10.1109/ICBK.2017.31",
language = "英语",
series = "Proceedings - 2017 IEEE International Conference on Big Knowledge, ICBK 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "264--269",
editor = "Xindong Wu and Xindong Wu and Tamer Ozsu and Jim Hendler and Ruqian Lu",
booktitle = "Proceedings - 2017 IEEE International Conference on Big Knowledge, ICBK 2017",
address = "美国",
}