Abstract
Emotion expression extraction is one of the important tasks of fine-grained sentiment mining. Existing methods lack efficiency in dealing with this task in Chinese microblogs because there are many new words and non-standard words in them. It’s found in this paper that a large number of new words are distributed in emotional expressions of the text in Chinese microblogs. A combined extraction model based on CRF is proposed, which incorporates new word detection into the task to improve the original work. The experimental results show that new word detection has good correlation with emotion expression extraction from Chinese microblogs, and that F1 value increases more than 2% on both the data sets of the movie field and the open field in Chinese microblogs.
Abstract
Emotion expression extraction is one of the important tasks of fine-grained sentiment mining. Existing methods lack efficiency in dealing with this task in Chinese microblogs because there are many new words and non-standard words in them. It’s found in this paper that a large number of new words are distributed in emotional expressions of the text in Chinese microblogs. A combined extraction model based on CRF is proposed, which incorporates new word detection into the task to improve the original work. The experimental results show that new word detection has good correlation with emotion expression extraction from Chinese microblogs, and that F1 value increases more than 2% on both the data sets of the movie field and the open field in Chinese microblogs.
Open Access
JUSTC
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