1. Identifying Sarcasm in Twitter: A Closer Look
Roberto Gonzalez Smaranda Muresan Nina Wacholder

2. Aim of the study
To construct a corpus of sarcastic utterances that have been explicitly labeled so by the composers themselves. (#sarcasm, #sarcastic)
To exemplify the difficulty in distinguishing sarcastic sentences from negative/positive sentences.

3. Data
Data for the study is divided in three sets of 900 tweets each: sarcastic, positive and negative.
Each data set is culled from twitter using appropriate hash-tags.
Sarcasm: #sarcasm, #sarcastic
Positive: #happy, #joy, #lucky
Negative: #sadness, #frustrated, #angry

4. Data Preprocessing
Tweets tagged with #sarcasm or #sarcastic in the middle of the tweet removed.
Manually checked to see if the tags were a part of the content of the tweet.
Eg: “I really love #sarcasm”

5. Lexical features Unigrams Dictionary based Pennebaker et al (LIWC)
Linguistic Processes (adverbs, pronouns)
Psychological Processes (Positive, negative emotion)
Personal Concerns (work, achievement)
Spoken Categories ( assent, non-fluencies)
WordNet Affect
List of interjections and punctuations

6. Pragmatic Features Positive emoticons Negative emoticons ToUser
smileys
Negative emoticons
Frowning faces
ToUser
@user

7. Comparisons and X2 rankings

8. Classification
Logistic Regression and Support Vector Machine with SMO (sequential minimal optimization)
Features used:
Unigrams
Dictionary features presence (LIWC+_P)
Dictionary features frequency (LIWC+_F)

9. Classification Results

10. Comparison against human performance
3 judges asked to classify tweets as sarcastic, positive or negative. (90 tweets per category)
S-N-P: 50% agreement (k = )
S-NS: 71.67% agreement (k = )
Emoticon based S-NS: 89% agreement (k = 0.74)

11. Human Comparison results