{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/sentiment-analysis-of-citations-using","title":"Sentiment Analysis of Citations Using Word2vec","arxiv_id":"1704.00177","date":"2017-04-01","proceeding":null,"authors":["Haixia Liu"],"abstract":"Citation sentiment analysis is an important task in scientific paper\nanalysis. Existing machine learning techniques for citation sentiment analysis\nare focusing on labor-intensive feature engineering, which requires large\nannotated corpus. As an automatic feature extraction tool, word2vec has been\nsuccessfully applied to sentiment analysis of short texts. In this work, I\nconducted empirical research with the question: how well does word2vec work on\nthe sentiment analysis of citations? The proposed method constructed sentence\nvectors (sent2vec) by averaging the word embeddings, which were learned from\nAnthology Collections (ACL-Embeddings). I also investigated polarity-specific\nword embeddings (PS-Embeddings) for classifying positive and negative\ncitations. The sentence vectors formed a feature space, to which the examined\ncitation sentence was mapped to. Those features were input into classifiers\n(support vector machines) for supervised classification. Using\n10-cross-validation scheme, evaluation was conducted on a set of annotated\ncitations. The results showed that word embeddings are effective on classifying\npositive and negative citations. However, hand-crafted features performed\nbetter for the overall classification.","url_abs":"http://arxiv.org/abs/1704.00177v1","url_pdf":"http://arxiv.org/pdf/1704.00177v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"sentiment-analysis-of-citations-using","repo_url":"https://github.com/liuhaixiachina/Sentiment-Analysis-of-Citations-Using-Word2vec","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"feature-engineering","task_name":"Feature Engineering"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"},{"task_slug":"word-embeddings","task_name":"Word Embeddings"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}