Abstract
Multiple emotions are often triggered in readers in response to text stimuli like news article. In this paper, we present a novel method for classifying news sentences into multiple emotion categories using an ensemble based multi-label classification technique called RAKEL. The emotion data consists of 1305 news sentences and the emotion classes considered are disgust, fear, happiness and sadness. Words are the most obvious choice as feature for emotion recognition. In addition to that we have introduced two novel feature sets: polarity of subject, verb and object of the sentences and semantic frames. Experiments concerning the comparison of features revealed that semantic frame feature combined with polarity based feature performs best in emotion classification. Experiments on feature selection over word and semantic frame features have been performed in order to handle feature sparseness problem. In both word and semantic frame feature, improvements in the overall performance have been observed after optimal feature selection.
Highlights
Emotion can be analyzed from two different perspectives: From the writer/speaker perspective, where we need to understand the emotional intent of the writer/speaker and from the reader’s perspective, where we try to identify the emotion that is evoked in a reader in response to a language stimulus
Study of suitable features: Emotion analysis of text being in its infancy, appropriate feature set required for emotion analysis has not been investigated properly
We have presented a multi-label classification based emotion analysis model
Summary
Emotion can be analyzed from two different perspectives: From the writer/speaker perspective, where we need to understand the emotional intent of the writer/speaker and from the reader’s perspective, where we try to identify the emotion that is evoked in a reader in response to a language stimulus. We aim at performing sentence level emotion analysis from a reader’s perspective which includes the following challenges. Triggering of multiple emotions: Given a sentence, a mix of multiple emotions can be triggered in a reader. The following sentence may evoke fear and sad emotion in readers mind. According to this theory, each news item is shaped into a form of story with layered dramatic frames, e.g., fear caused by danger; sorrow and grief arising from violence, crime and death; exhilaration and joy resulting from good luck or victory. Amount of news articles capable of evoking emotions in readers is huge. We have rested our study on a set of sentences collected from news articles and headlines
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