Round Corner
Department of Computer and Information Science


Sentiment Analysis of Figurative Language in Twitter

The master thesis project is aimed at the automatic classification of tweets containing figurative language, that is, language which intentionally conveys secondary or extended meanings (such as sarcasm, irony and metaphor). Such figurative language creates a significant challenge for sentiment analysis systems, as direct approaches based on words and their lexical semantics often are inadequate in the face of indirect meanings. One goal of the project is to find a set of tweets that are rich in figurative language, another goal is to determine whether the writer of each such tweet has expressed a positive or negative sentiment, and possibly the degree to which this sentiment has been communicated.

For the data collection part, the project could tentatively build on data sets from the Semantic Evaluation (SemEval) shared task exercises, in particular the tweets annotated for figurative language in SemEval-15 (Task 11) and those annotated for sarcasm in the SemEval tasks on Twitter sentiment analysis (Sem-14 Task 9, SemEval-15 Task 10, SemEval-16 Task 4).


Björn Gambäck Björn Gambäck
315 IT-bygget
735 93354 
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