{"type":"ScholarlyArticle","title":"Positive, Negative, or Neutral: Learning an Expanded Opinion Lexicon from Emoticon-Annotated Tweets","authors":[{"name":"Felipe Bravo-Marquez","slug":"felipe-bravo-marquez","given":"Felipe","family":"Bravo-Marquez","affiliation":""},{"name":"Eibe Frank","slug":"eibe-frank","given":"Eibe","family":"Frank","affiliation":""},{"name":"Bernhard Pfahringer","slug":"bernhard-pfahringer","given":"Bernhard","family":"Pfahringer","affiliation":""}],"abstract":"We present a supervised framework for expanding an opinion lexicon for tweets. The lexicon contains part-of-speech (POS) disambiguated entries with a three-dimensional probability distribution for positive, negative, and neutral polarities. To obtain this distribution using machine learning, we propose word-level attributes based on POS tags and information calculated from streams of emoticon-annotated tweets. Our experimental results show that our method outperforms the three-dimensional word-level polarity classification performance obtained by semantic orientation, a state-of-the-art measure for establishing world-level sentiment.","doi":null,"url":"https://ijcai.org/proceedings/2015/177","pdf":"https://ijcai.org/proceedings/2015/0177.pdf","markdown":"https://ijcai.org/proceedings/2015/177.md","bibtex":"https://ijcai.org/proceedings/2015/bibtex/177","volume":{"slug":"2015","title":"Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2015","url":"https://ijcai.org/proceedings/2015/","year":2015,"location":"Buenos Aires, Argentina","dates":"25–31 July 2015","editors":"Qiang Yang and Michael Wooldridge","isbn":"978-1-57735-738-4","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Natural Language Processing","track":"Main Track","number":177,"pages":{"first":1229,"last":1234},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}