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Abstract:
General Purpose Emotion Lexicons (GPELs) that associate words with emotion categories remain a valuable resource for emotion analysis of text.
However the static and formal nature of their vocabularies make them inadequate for extracting effective features for document representation, in domains that are inherently dynamic in nature (e.g. Social Media).
This calls for lexicons that are not only adaptive to the lexical variations in a domain but also provide finer-grained quantitative estimates to accurately capture word-emotion associations.
In this paper we extend prior work on domain specific emotion lexicon (DSEL) generation and apply it for emotion feature extraction.
We demonstrate how our generative unigram mixture model (UMM) based DSEL learnt by harnessing labelled (blogs, news headlines and incident reports) and weakly-labelled (tweets) emotion text can be used to extract effective features for emotion classification.
Our results confirm that the features derived using the proposed lexicon outperform those from state-of-the-art lexicons learnt using supervised Latent Dirichlet Allocation (sLDA) and Point-Wise Mutual Information (PMI).
Further the proposed lexicon features also outperform state-of-the-art features derived using a combination of n-grams, part-of-speech information and sentiment lexicons.
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https://www.sciencedirect.com/science/article/abs/pii/S0167865516303567
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