Sentiment analysis of text data has become very much popular in past few years, because of the interesting challenges it can offer. Among these challenges, sarcasm is unique. We can address sarcasm as the ’Achilles heel ’ of sentiment analysis. Detection of sarcasm from the given data is same time complicated and interesting. It is interesting because, if researchers can find the optimized solutions for finding sarcastic words in the data, it will enhance the sentiment analysis of that data. Even humans also have difficulties in understanding the actual interpretation of a sentence, if it is presented indirectly. This means, a person stated something, but he meant contradictory to the word meaning of the statement. This makes sarcasm detection an interesting topic for researchers. In this paper, we evaluated the performance of several machine learning models like Support vector machine, Naïve Bayes, Decision tree etc., and different ensemble models like Random Forest, XGBoost and AdaBoost etc., with the collaboration of various feature extraction methods such as Term Frequency-Inverse Document Frequency etc. The main evaluation metrics we used to evaluate the performance are accuracy, precision, f-score and recall. Based on the results, we concluded that the models such as XGBoost, LightGBM and Bagging classifiers provide better results in detecting sarcasm with respect to other machine learning models.
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23 May 2023
INTERNATIONAL CONFERENCE ON HUMANS AND TECHNOLOGY: A HOLISTIC AND SYMBIOTIC APPROACH TO SUSTAINABLE DEVELOPMENT: ICHT 2022
17–22 January 2022
Kochi, Kerala, India
Research Article|
May 23 2023
Performance analysis of various sarcasm detection algorithms based on feature extraction methods
Jihad Aboobaker;
Jihad Aboobaker
a)
1
Department of Computer science and Engineering Puducherry Technological University
, Kalapet, Puducherry-605014, India
a)Corresponding author: [email protected]
Search for other works by this author on:
E. Ilavarasan
E. Ilavarasan
b)
1
Department of Computer science and Engineering Puducherry Technological University
, Kalapet, Puducherry-605014, India
Search for other works by this author on:
a)Corresponding author: [email protected]
AIP Conf. Proc. 2773, 020008 (2023)
Citation
Jihad Aboobaker, E. Ilavarasan; Performance analysis of various sarcasm detection algorithms based on feature extraction methods. AIP Conf. Proc. 23 May 2023; 2773 (1): 020008. https://doi.org/10.1063/5.0138753
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