Stance Detection on Tweets: An SVM-based Approach
Table 4. Statistical Information on the Named Entities Annotated in the Three Versions of the Stance-Annotated Tweet Data Sets.
['Stance Data Set', 'Target', 'Stance Class', '# of Tweets', '# of Named Entities Person', '# of Named Entities Location', '# of Named Entities Organization', '# of Named Entities  [BOLD] Total']
Named Entity Recognition Experiments on the Stance Data Sets In order to carry out NER experiments on the three versions of our stance data sets, we have manually annotated them to use them as the answer keys. The named entity types considered are person, location, and organization names. The most common named entity type is organization names as expected, since each tweet contains the stance target (i.e., the football club name) explicitly and they are named entities of organization name type. Yet, they are favorable because the performance of NER tools is known to decrease considerably when ported to tweets The following two conclusions can be drawn based on these results: Overall, the results are better than those NER results reported on the randomly-compiled tweet sets. This may be attributed to the fact that all of the tweets in the three versions of our stance data sets contain the stance targets (the club names) explicitly, making our procedure ”targeted” NER. The NER performance is far better on tweets marked with the Favor stance class when compared with the performance on tweets marked as Against. The named entities in the tweets from the Favor seem to be commonly well-formed and canonical names of these entities. The considerable difference in the recall rates for these two groups supports this argument.