1. A PROJECT REPORT ON
SARCASM ANALYSIS USING
MACHINE LEARNING
Submitted by:
Subhadarsini prusty
B212045, IIIT BBSR

2. ABSTRACT
Sarcasm analysis being one of the toughest challenges in Natural
Language Processing (NLP) , has been a hot topic of research these
years. A lot of work has already been done in the field of sentiment
analysis but still there are enormous challenges to it. The property of
sarcasm that makes it difficult to analyze and detect is the gap
between its literal and intended meaning. Detecting sentiment in social
media texts like facebook, twitter, online blogs, reviews has become an
important task as they influence every business organizations. In this
work I have presented the detection of sarcasm in tweets using
machine learning approach.
INTRODUCTION
Wikipedia defines sentiment analysis as the process that “aims to
determine the attitude of a speaker or a writer with respect to some
topic”. Social media and social networking have fueled the online
space. Ratings, reviews, comments etc are everywhere. The need for
clear, reliable information about consumer preferences has led to
increasing interests in high level analysis of online social media content.
For many businesses, online opinion has turned into a kind of virtual
currency that can make or break a product in the marketplace.
Sentiment analysis actually means monitoring social media posts and
discussions, then figuring out how participants are reacting to a brand
or event.
Sarcasm is derived from the French word “Sarcasmor” that means “tear
flesh” or “grind the teeth”. In simple words it means to speak bitterly.
The literal meaning is different than what the speaker intends to say

3. through sarcasm. As defined by Wikipedia sources sarcasm is a “sharp,
bitter, or cutting expression or remark; a bitter gibe or taunt”. Sarcasm
is also defined as a contrast between a positive sentiment and negative
situation and vice versa. For example, “I love working on holidays”. In
this sentence “love” is giving a positive sentiment but “working on
holidays” is referring to a negative situation since people generally used
to relax on holidays. Sarcasm is known as “the activity of saying or
writing the opposite of what you mean, or of speaking in a way
intended to make someone else feel stupid or show them that you are
angry” (Macmilan,2007). Example: “it feels great being bored”. The
sentence contains both positive (“great”) and negative word (“bored”)
in a sentence. Therefore, it can be classified as a sarcastic sentence.
For the task of sarcasm analysis it is very important to have a
rudimentary knowledge of Natural Language Processing (NLP). NLP
aims to acquire, understand and generate the human languages such as
English, Chinese etc. The natural language analysis goes through many
stages namely tokenization, lexical analysis, syntactic analysis, semantic
analysis, and pragmatic analysis. Lexical analysis is converting a
sequence of characters into a sequence of tokens i.e. meaningful
character strings. Syntactic analysis provides an order and structure of
each sentence in the text. Semantic analysis is to find the literal
meaning, and pragmatic analysis is to determine the meaning of the
text in context. These tasks are further broken down into parsing, pos-
tagging etc. There are challenges in every field of NLP and extensive
research works are going on for these. Some of the challenges include:
POS-tagging, named entity recognition, sentiment analysis, coreference
resolution, parsing, machine translation, information translation, text
summarization etc.

4. Sarcasm can be detected considering certain features. There are
pragmatic and lexical features. Features can also be developed using
certain patterns. There can be features based on oral or gestural clues
such as: emoticons, onomatopoeic expressions for laughter, positive
interjections, quotation marks, use of punctuations, can also help in
detecting sarcasm. But all these features are not enough to identify
sarcasm in a piece of text. There is a need of context, a common ground
between tweeters and general world knowledge. The machine should
be aware of what is the context of the text and relating it to general
world knowledge in order to identify sarcasm more accurately. In my
work I have taken some of the features like intensifiers and interjection
words and through different machine learning algorithms checked how
well the model is performing.
LITERATURE REVIEW
(Francesco Barbieri, Francesco Ronzano, Horracio sagion) considered 7
sets of lexical features aim to detect sarcasm through its inner structure
such as unexpectedness, intensity of the terms or imbalance between
registers etc. They haven’t used any pattern of words as features. Irony
and sarcasm has been approached as computation problem recently by
(Carvalho et al) who created an automatic system for detecting irony
relying on emoticons and special punctuations. They focused on
detection of ironic style in newspaper articles. (Lukin and Walker) used
bootstrapping to improve the performance of sarcasm and nastiness
classifiers for online dialogue and (Liebrecht et al) designed a model to
detect sarcasm in Dutch tweets. Finally (Riloff) built a model to detect
sarcasm with a bootstrapping algorithm that automatically learn lists of
positive sentiment phrases and negative situation phrases from

5. sarcastic tweet, in order to detect the characteristic of sarcasm of being
a contrast between positive sentiment and negative situation.(Pang and
Lee) used a supervised approach to classify movie reviews into two
classes after performing subjective feature extraction. They achieve an
in-language classification accuracy of 86% using unigram model. In
contrast (Dave et al) find that bigram based features produce better
results.(Chaumartin et al) use sentiwordnet to for finding the polarity of
the newspaper headings. (Verma and Bhattacharyya et al) used the
same resource for developing both resource-based and machine
learning-based methods for classification of movie reviews.
METHODOLOGY
The most important task for sarcasm detection is to prepare a feature
list according to which the machine will be trained to detect sarcastic
and non sarcastic tweets. I have taken a list of interjection words and
intensifiers to build my feature vector. The algorithm for the feature
vector is as follows:
Input- preprocessed sarcastic tweets.
Output- interjection words and intensifiers from the sarcastic tweets.
1.def getFeatureVector(tweet):
2. featureVector=[]
3. words=tweet.split()
4. words_in=[wow,aww,poo,yay,oh,yeah,very,really,extremely,….]
5. for w in words:
6. for I in words_in:

6. 7. if (w==I) :
8. featureVector.append(w)
9. return featureVector
#end
Once a feature vector is ready rest of the things become easy. The
quality of our result will solely depend on the feature vector made. The
entire process is as below.
1. Tweets are preprocessed i.e tweets are converted to lower cases,
URLs and hashtags are removed, @username are replaced by ATUSER
etc.
2. Stopwords are removed and feature vector is made as shown above.
3. From the file, the tweets and labels are extracted and a feature
vector is obtained from them.
4. A feature list is made that consist of all feature words extracted from
tweets.
5. After this the training set is made ready with the help of Natural
Language Processing Toolkit (NLTK) and using those extracted features
from tweets.
6. Once the training set gets ready now the tweets are classified using
different classifiers such as Naïve Bayes, Maximum entropy and
Decision tree classifiers and accuracies are noted and analyzed.
RESULTS
The feature vector will appear as

7. For training 5000 sarcastic and 5000 non sarcastic tweets are taken. For
good results it is important to have a balanced corpus. And for testing
2000 sarcastic and 2000 non sarcastic tweets are taken. The accuracies
of the classifiers are found as
CLASSIFIERS ACCURACY RECALL PRECISION F-SCORE
Naïve Bayes 0.55 0.43 0.41 0.42
Maximum entropy 0.60 0.47 0.46 0.46
Decision Tree 0.63 0.51 0.42 0.46
SARCASTIC TWEETS FEATURE WORDS
oh really!! I did not know that
before. Thanks for making me
informed.
‘oh’, ’really’
aww! I love working on holidays. ‘aww’
yay! Thoroughly enjoy the smell of
pot permeating through my house
while clean, thanks neighbours.
‘yay’
being ignored makes me extremely
happy. yeah!!
‘extremely’, ’yeah’

8. CONCLUSION
Since I managed to get an average 50%-60% accuracy, it can be made
far better by incorporating better and detailed features. The better is
the feature vector, better will be the model. Also an important factor
for the model’s performance is the corpus. Corpus should be chosen
wisely to get maximum efficiency and accuracy.
FUTURE WORK
I would try and bring in more detailed features to detect sarcasm more
precisely. Other than this there are also other aspects of sarcasm
analysis to be worked upon.
 Coreference resolution.
 Identify context in written data for more accurate sarcasm
detection.
 Identify sarcasm in text for Indian languages.
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