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- W2124335969 abstract "This paper describes our contribution to the SemEval-2014 Task 9 on sentiment analysis in Twitter. We participated in both strands of the task, viz. classification at message-level (subtask B), and polarity disambiguation of particular text spans within a message (subtask A). Our experiments with a variety of lexical and syntactic features show that our systems benefit from rich feature sets for sentiment analysis on user-generated content. Our systems ranked ninth among 27 and sixteenth among 50 submissions for task A and B respectively." @default.
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- W2124335969 date "2014-01-01" @default.
- W2124335969 modified "2023-10-16" @default.
- W2124335969 title "LT3: Sentiment Classification in User-Generated Content Using a Rich Feature Set" @default.
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- W2124335969 doi "https://doi.org/10.3115/v1/s14-2070" @default.
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