期刊名称：Conference on European Chapter of the Association for Computational Linguistics (EACL)
出版年度：2011
卷号：2011
出版社：ACL Anthology
摘要：Sentiment analysis on Twitter data has attracted
much attention recently. In this paper, we
focus on target-dependent Twitter sentiment
classification; namely, given a query, we classify
the sentiments of the tweets as positive,
negative or neutral according to whether they
contain positive, negative or neutral sentiments
about that query. Here the query serves
as the target of the sentiments. The state-ofthe-
art approaches for solving this problem
always adopt the target-independent strategy,
which may assign irrelevant sentiments to the
given target. Moreover, the state-of-the-art
approaches only take the tweet to be classified
into consideration when classifying the sentiment;
they ignore its context (i.e., related
tweets). However, because tweets are usually
short and more ambiguous, sometimes it is not
enough to consider only the current tweet for
sentiment classification. In this paper, we propose
to improve target-dependent Twitter sentiment
classification by 1) incorporating
target-dependent features; and 2) taking related
tweets into consideration. According to the
experimental results, our approach greatly improves
the performance of target-dependent
sentiment classification.
