摘要
表情符作为一种新兴的网络语言,受到了越来越多的微博用户的青睐。微博中出现的表情符形象直观地表达了博主的情绪,对情绪分析起着至关重要的作用。首先对大量中文微博中表情符的使用特点、分布情况和情绪表达特点进行了统计分析。然后,人工选取具有代表性且情感倾向明确的表情符作为六类基本情绪的种子表情符。根据目标表情符和六类情绪的种子表情符在微博文本中的共现情况,为其建立六维情绪向量,并将其应用于微博情绪分析。在两个数据集上的实验结果表明,本文建立的表情符情绪向量有效地提高了微博情绪识别的精度。
As a new network language,emoticons have earned the favor of an increasing number of Micro-blog users.Emoticons in micro-blogs vividly represent blogger＇s emotions.We first make a comprehensive analysis of emoticons in a large corpus of Chinese micro-blogs,including their usage,distribution and characteristics in emotion expression.Secondly,we manually select a list of emoticons that typically indicate six basic emotions as seeds.Based on the co-occurrence between a target emoticon and the seed emoticons in a large corpus,we establish six-dimensioned vectors for the target emoticon and apply them to emotion analysis.Experimental results on two data sets show that the emoticon vectors can effectively improve the precision of micro-blog emotion recognition.
出处
《计算机工程与科学》
CSCD
北大核心
2016年第3期577-584,共8页
Computer Engineering & Science
基金
国家自然科学基金(61202132)
关键词
表情符
情绪向量
统计分析
情绪分析
emoticon
emotion vectors
statistical analysis
emotion analysis
