Positive, negative, or neutral: learning an expanded opinion lexicon from emoticon-annotated tweets
Year: 2015
Type: conference-paper
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 pos-itive, negative, and neutral polarities. To obtain this distribution using machine learning, we pro-pose 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 ob-tained by semantic orientation, a state-of-the-art measure for establishing world-level sentiment. 1
Institution: University of Waikato
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2221308652