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Affect Analysis of Web Forums and Blogs Using Correlation Ensembles
September 2008 (vol. 20 no. 9)
pp. 1168-1180
ASCII Text
x 
 
Ahmed Abbasi, Hsinchun Chen, Sven Thoms, Tianjun Fu, "Affect Analysis of Web Forums and Blogs Using Correlation Ensembles," IEEE Transactions on Knowledge and Data Engineering, vol. 20, no. 9, pp. 1168-1180, September, 2008.
 
 
BibTex
x
 
@article{ 10.1109/TKDE.2008.51,
author = {Ahmed Abbasi and Hsinchun Chen and Sven Thoms and Tianjun Fu},
title = {Affect Analysis of Web Forums and Blogs Using Correlation Ensembles},
journal ={IEEE Transactions on Knowledge and Data Engineering},
volume = {20},
number = {9},
issn = {1041-4347},
year = {2008},
pages = {1168-1180},
doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2008.51},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Knowledge and Data Engineering
TI - Affect Analysis of Web Forums and Blogs Using Correlation Ensembles
IS - 9
SN - 1041-4347
SP1168
EP1180
EPD - 1168-1180
A1 - Ahmed Abbasi,
A1 - Hsinchun Chen,
A1 - Sven Thoms,
A1 - Tianjun Fu,
PY - 2008
KW - Text mining
KW - Discourse
KW - Machine learning
KW - Linguistic processing
VL - 20
JA - IEEE Transactions on Knowledge and Data Engineering
ER -
 
Ahmed Abbasi, University of Arizona, Tucson
Hsinchun Chen, University of Arizona, Tucson
Sven Thoms, University of Arizona, Tucson
Tianjun Fu, University of Arizona, Tucson
Analysis of affective intensities in computer mediated communication is important in order to allow a better understanding of online users? emotions and preferences. Despite considerable research on textual affect classification, it is unclear which features and techniques are most effective. In this study we compared several feature representations for affect analysis, including learned n-grams and various automatically and manually crafted affect lexicons. We also proposed the support vector regression correlation ensemble (SVRCE) method for enhanced classification of affect intensities. SVRCE uses an ensemble of classifiers each trained using a feature subset tailored towards classifying a single affect class. The ensemble is combined with affect correlation information to enable better prediction of emotive intensities. Experiments were conducted on four test beds encompassing web forums, blogs, and online stories. The results revealed that learned n-grams were more effective than lexicon based affect representations. The findings also indicated that SVRCE outperformed comparison techniques, including Pace regression, semantic orientation, and WordNet models. Ablation testing showed that the improved performance of SVRCE was attributable to its use of feature ensembles as well as affect correlation information. A brief case study was conducted to illustrate the utility of the features and techniques for affect analysis of large archives of online discourse.

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Index Terms:
Text mining, Discourse, Machine learning, Linguistic processing
Citation:
Ahmed Abbasi, Hsinchun Chen, Sven Thoms, Tianjun Fu, "Affect Analysis of Web Forums and Blogs Using Correlation Ensembles," IEEE Transactions on Knowledge and Data Engineering, vol. 20, no. 9, pp. 1168-1180, Sept. 2008, doi:10.1109/TKDE.2008.51
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