计算机科学技术学报(英文版)
計算機科學技術學報(英文版)
계산궤과학기술학보(영문판)
JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
2015年
5期
1120-1129
,共10页
姜飞%刘奕群%孙甲申%朱璇%张敏%马少平%Shao-Ping Ma
薑飛%劉奕群%孫甲申%硃璇%張敏%馬少平%Shao-Ping Ma
강비%류혁군%손갑신%주선%장민%마소평%Shao-Ping Ma
microblog sentiment analysis%emoticon space%polarity classification%subjectivity classification%emotion clas-sification
Emoticons have been widely employed to express different types of moods, emotions, and feelings in microblog environments. They are therefore regarded as one of the most important signals for microblog sentiment analysis. Most existing studies use several emoticons that convey clear emotional meanings as noisy sentiment labels or similar sentiment indicators. However, in practical microblog environments, tens or even hundreds of emoticons are frequently adopted and all emoticons have their own unique emotional meanings. Besides, a considerable number of emoticons do not have clear emotional meanings. An improved sentiment analysis model should not overlook these phenomena. Instead of manually assigning sentiment labels to several emoticons that convey relatively clear meanings, we propose the emoticon space model (ESM) that leverages more emoticons to construct word representations from a massive amount of unlabeled data. By projecting words and microblog posts into an emoticon space, the proposed model helps identify subjectivity, polarity, and emotion in microblog environments. The experimental results for a public microblog benchmark corpus (NLP&CC 2013) indicate that ESM effectively leverages emoticon signals and outperforms previous state-of-the-art strategies and benchmark best runs.