  Citation sentiment analysis is an important task in scientific paper
analysis. Existing machine learning techniques for citation sentiment analysis
are focusing on labor-intensive feature engineering, which requires large
annotated corpus. As an automatic feature extraction tool, word2vec has been
successfully applied to sentiment analysis of short texts. In this work, I
conducted empirical research with the question: how well does word2vec work on
the sentiment analysis of citations? The proposed method constructed sentence
vectors (sent2vec) by averaging the word embeddings, which were learned from
Anthology Collections (ACL-Embeddings). I also investigated polarity-specific
word embeddings (PS-Embeddings) for classifying positive and negative
citations. The sentence vectors formed a feature space, to which the examined
citation sentence was mapped to. Those features were input into classifiers
(support vector machines) for supervised classification. Using
10-cross-validation scheme, evaluation was conducted on a set of annotated
citations. The results showed that word embeddings are effective on classifying
positive and negative citations. However, hand-crafted features performed
better for the overall classification.
