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• W2156413587 abstract "We describe a state-of-the-art sentiment analysis system that detects (a) the sentiment of short informal textual messages such as tweets and SMS (message-level task) and (b) the sentiment of a word or a phrase within a message (term-level task). The system is based on a supervised statistical text classification approach leveraging a variety of surface-form, semantic, and sentiment features. The sentiment features are primarily derived from novel high-coverage tweet-specific sentiment lexicons. These lexicons are automatically generated from tweets with sentiment-word hashtags and from tweets with emoticons. To adequately capture the sentiment of words in negated contexts, a separate sentiment lexicon is generated for negated words. The system ranked first in the SemEval-2013 shared task `Sentiment Analysis in Twitter' (Task 2), obtaining an F-score of 69.02 in the message-level task and 88.93 in the term-level task. Post-competition improvements boost the performance to an F-score of 70.45 (message-level task) and 89.50 (term-level task). The system also obtains state-of-the-art performance on two additional datasets: the SemEval-2013 SMS test set and a corpus of movie review excerpts. The ablation experiments demonstrate that the use of the automatically generated lexicons results in performance gains of up to 6.5 absolute percentage points." @default.
• W2156413587 created "2016-06-24" @default.
• W2156413587 creator A5016892586 @default.
• W2156413587 creator A5033684482 @default.
• W2156413587 creator A5077738658 @default.
• W2156413587 date "2014-08-20" @default.
• W2156413587 modified "2023-10-14" @default.
• W2156413587 title "Sentiment Analysis of Short Informal Texts" @default.
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• W2156413587 doi "https://doi.org/10.1613/jair.4272" @default.
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