SentiCite \subtitleAn Approach for Publication Sentiment Analysis
Table 2: SentiCiteDB.
['[EMPTY]', '[BOLD] Total', '[BOLD] Train set', '[BOLD] Test set']
SentiCiteDB is a dataset of publication sentiment analysis which is created using Scientific publications from International Conference on Document Analysis and Recognition (ICDAR) 2013. Sentences with citation are manually extracted from the publication and their manual ground truth is created. In total, SentiCiteDB contains about 2100 citations with 210 positive, 1805 neutral and 85 negative sentiment scores. Out of these, 50 citations from each of the three classes of sentiment are used for training of SentiCite. This dataset includes references from different sections of the document to make sure that the proposed method does not learn the sentiment classification for one specific section. The training set was selected with the same number of references for all classes to ensure that the classifier learns the classes in a sufficient way. Providing a real-world distribution a training set resulted in bad results for negative and positive labels.