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Dimensional Sentiment Analysis of Traditional Chinese Words Using Pre-trained Not-quite-right Sentiment Word Vectors and Supervised Ensemble Models

Abstract: 

This work focuses on two specific types of sentimental information analysis for traditional Chinese words, i.e., valence represents the degree of pleasant and unpleasant feelings (i.e., sentiment orientation), and arousal represents the degree of excitement and calm (i.e., sentiment strength). To address it, we proposed supervised ensemble learning models to assign appropriate real valued ratings to each
word on two sentimental dimensions, incorporating pretrained semantic and sentiment word vectors into the models. Experimental results on IALP 2016 Shared Task data set showed that our method achieves desirable performance in predicting real valued ratings of given words in valence subtask and forecasting the order of words in arousal subtask. Specifically, for the valence subtask, our system ranks the first in terms of MAE measure.

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Paper Details

Authors:
Feixiang Wang, Yunxiao Zhou, Lan man
Submitted On:
27 November 2016 - 11:06pm
Short Link:
Type:
Presentation Slides
Event:
Paper Code:
117
Document Year:
2016
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IALP-117-slides

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[1] Feixiang Wang, Yunxiao Zhou, Lan man, "Dimensional Sentiment Analysis of Traditional Chinese Words Using Pre-trained Not-quite-right Sentiment Word Vectors and Supervised Ensemble Models", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1314. Accessed: Jun. 25, 2017.
@article{1314-16,
url = {http://sigport.org/1314},
author = {Feixiang Wang; Yunxiao Zhou; Lan man },
publisher = {IEEE SigPort},
title = {Dimensional Sentiment Analysis of Traditional Chinese Words Using Pre-trained Not-quite-right Sentiment Word Vectors and Supervised Ensemble Models},
year = {2016} }
TY - EJOUR
T1 - Dimensional Sentiment Analysis of Traditional Chinese Words Using Pre-trained Not-quite-right Sentiment Word Vectors and Supervised Ensemble Models
AU - Feixiang Wang; Yunxiao Zhou; Lan man
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1314
ER -
Feixiang Wang, Yunxiao Zhou, Lan man. (2016). Dimensional Sentiment Analysis of Traditional Chinese Words Using Pre-trained Not-quite-right Sentiment Word Vectors and Supervised Ensemble Models. IEEE SigPort. http://sigport.org/1314
Feixiang Wang, Yunxiao Zhou, Lan man, 2016. Dimensional Sentiment Analysis of Traditional Chinese Words Using Pre-trained Not-quite-right Sentiment Word Vectors and Supervised Ensemble Models. Available at: http://sigport.org/1314.
Feixiang Wang, Yunxiao Zhou, Lan man. (2016). "Dimensional Sentiment Analysis of Traditional Chinese Words Using Pre-trained Not-quite-right Sentiment Word Vectors and Supervised Ensemble Models." Web.
1. Feixiang Wang, Yunxiao Zhou, Lan man. Dimensional Sentiment Analysis of Traditional Chinese Words Using Pre-trained Not-quite-right Sentiment Word Vectors and Supervised Ensemble Models [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1314