SentiCite \subtitleAn Approach for Publication Sentiment Analysis
Table 5: Evaluation of different features for SentiCite (SC).
['[BOLD] Label', '[BOLD] SC-SVM', '[BOLD] SC-Paum']
The purpose of the feature extraction step is to get a meaningful set of features to train the classifiers. Besides the string of the tokens, three additional feature extraction modules were used. The POS tagger assigns different features e.g., type of the token, length or capitalization. The type of the token helps to indicate the importance of the token e.g., an adjective is more relevant than an article or nouns. The last module is a stemming tool for a more generalized representation of the different tokens. This procedure was done for the sentiment and the nature of citation. Furthermore, it has to be mentioned that features like n-gram, hyponyms, hypernyms and others were tested but the performance of these features was not sufficient. It is important to note here, that, a classifier can often perform well on the neutral classes and bad on the positive and negative classes but, it can still result in a good overall accuracy because of the disproportionate ratio of neutral and positive or negative references in a scientific document. For more meaningful results further experiments were performed.