Can Korean Functional Suffixes Trigger Sentiment in Short Reviews?
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초록
Grammatical categories contributed to sentiment analysis have been mostly a word or a bag of words which express semantic orientation. Korean, however, presents some of the sentence-final suffixes to be exploited for identifying sentiment. Those suffixes mark honorification which are interpreted as lowered or raised speech depending on their type. This research has referred them as Sentiment-Triggering Suffixes (STSs) and extracted them from a large sentiment corpus, utilizing Chi-square test and Association rules. A series of experiments has proved that using STSs as a feature is effective in detecting sentiment particularly when a sentence is short in length and does not contain a sentiment-bearing word. In addition, better results have been derived when STSs imposing negative sentiment were implemented. Utilizing STSs, in particular, helps identify rhetorical question, which is considered as a challenging task. Since cutting-edge machine learning techniques can visualize the most important features of a classifier, we have illustrated the characteristics of STSs using two of the techniques, t-SNE and heatmap.
Abstract
Grammatical categories contributed to sentiment analysis have been mostly a word or a bag of words which express semantic orientation. Korean, however, presents some of the sentence-final suffixes to be exploited for identifying sentiment. Those suffixes mark honorification which are interpreted as lowered or raised speech depending on their type. This research has referred them as Sentiment-Triggering Suffixes (STSs) and extracted them from a large sentiment corpus, utilizing Chi-square test and Association rules. A series of experiments has proved that using STSs as a feature is effective in detecting sentiment particularly when a sentence is short in length and does not contain a sentiment-bearing word. In addition, better results have been derived when STSs imposing negative sentiment were implemented. Utilizing STSs, in particular, helps identify rhetorical question, which is considered as a challenging task. Since cutting-edge machine learning techniques can visualize the most important features of a classifier, we have illustrated the characteristics of STSs using two of the techniques, t-SNE and heatmap.
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