This study examines the effectiveness of various feature representations and techniques for analyzing affective intensities in computer-mediated communication. It compares learned n-grams and different affect lexicons, and introduces the support vector regression correlation ensemble (SVRCE) method for enhanced affect intensity classification. SVRCE employs an ensemble of classifiers, each tailored to a specific affect class, and incorporates affect correlation information for improved predictions. Experiments on web forums, blogs, and online stories demonstrate that learned n-grams outperform lexicon-based representations and that SVRCE surpasses other techniques like Pace regression and WordNet models. Ablation testing confirms that SVRCE's success is due to its use of feature ensembles and affect correlation information. A case study highlights the utility of these features and techniques in analyzing large online discourse archives.
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Abbasi, Ahmed, et al. "Affect analysis of web forums and blogs using correlation ensembles." IEEE transactions on knowledge and data engineering 20.9 (2008): 1168-1180.
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