Stance detection is a subproblem of sentiment analysis where the stance of
the author of a piece of natural language text for a particular target (either
explicitly stated in the text or not) is explored. The stance output is usually
given as Favor, Against, or Neither. In this paper, we target at stance
detection on sports-related tweets and present the performance results of our
SVM-based stance classifiers on such tweets. First, we describe three versions
of our proprietary tweet data set annotated with stance information, all of
which are made publicly available for research purposes. Next, we evaluate SVM
classifiers using different feature sets for stance detection on this data set.
The employed features are based on unigrams, bigrams, hashtags, external links,
emoticons, and lastly, named entities. The results indicate that joint use of
the features based on unigrams, hashtags, and named entities by SVM classifiers
is a plausible approach for stance detection problem on sports-related tweets.

本研究探讨了面向体育相关推特的立场检测问题，并使用基于 SVM 的分类器在不同的特征集上进行了评估，结果表明将基于 unigram、hashtags 和 named entities 的特征组合使用于 SVM 分类器中是该问题的一个可行方法。