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Automatic Sarcasm Detection in Twitter Messages

Ræder, Johan Georg Cyrus Mazaher
Master thesis
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URI
http://hdl.handle.net/11250/2415329
Date
2016
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  • Institutt for datateknologi og informatikk [3877]
Abstract
In the past decade, social media like Twitter have become popular and

a part of everyday life for many people. Opinion mining of the thoughts

and opinions they share can be of interest to, e.g., companies and organizations.

The sentiment of a text can be drastically altered when figurative

language such as sarcasm is used. This thesis presents a system for automatic

sarcasm detection in Twitter messages.

To get a better understanding of the field, state-of-the-art systems for

detecting sarcasm in Twitter messages are explored. Many such systems

already exist, and a common theme among them is the use of automatically

annotated data for both training and testing. In addition to presenting a

system for detecting sarcasm, this thesis also looks into the use of manually

annotated data for testing. To this end, a dataset of tweets manually

annotated with respect to the presence of sarcasm was built. The result

was very similar to that of a previously made set, and both of them showed

considerable deviation from automatic annotation. This implies that using

automatically annotated data for the task of sarcasm detection in tweets

is a mediocre approximation.

Experiments with both of the manually annotated datasets also gave

very similar results, showing that they are well annotated and reasonably

representative for sarcasm detection in tweets.
Publisher
NTNU

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