TY - GEN
T1 - Beyond Face Value
T2 - 3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023
AU - Damaraju, Avaneesh Jai
AU - Rao, Divya
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - This research study presents a comparative analysis of multiclass classifiers for detecting irony and sarcasm in short texts. With the increasing use of social media, it has become important to develop accurate and efficient algorithms for detecting irony and sarcasm, which are often expressed implicitly in text. This study has evaluated five multiclass classifiers, including Naive Bayes, linear classifier, XG Boost, KNN, and SGD, using different text representations, such as count vectors, word-level TF-IDF, and hash vectors. The results showed that the linear classifier using word-level TF-IDF achieved the highest accuracy of 74.39%, while the KNN classifier using hash vectors achieved the highest accuracy of 75.16%. However, all classifiers exhibited sensitivity to certain keywords and phrases, indicating the need for further research to improve the robustness of the classifiers. This study provides insights into the strengths and weaknesses of different multiclass classification approaches for detecting irony and sarcasm in short texts, which can guide future research in this field.
AB - This research study presents a comparative analysis of multiclass classifiers for detecting irony and sarcasm in short texts. With the increasing use of social media, it has become important to develop accurate and efficient algorithms for detecting irony and sarcasm, which are often expressed implicitly in text. This study has evaluated five multiclass classifiers, including Naive Bayes, linear classifier, XG Boost, KNN, and SGD, using different text representations, such as count vectors, word-level TF-IDF, and hash vectors. The results showed that the linear classifier using word-level TF-IDF achieved the highest accuracy of 74.39%, while the KNN classifier using hash vectors achieved the highest accuracy of 75.16%. However, all classifiers exhibited sensitivity to certain keywords and phrases, indicating the need for further research to improve the robustness of the classifiers. This study provides insights into the strengths and weaknesses of different multiclass classification approaches for detecting irony and sarcasm in short texts, which can guide future research in this field.
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U2 - 10.1109/ICPCSN58827.2023.00018
DO - 10.1109/ICPCSN58827.2023.00018
M3 - Conference contribution
AN - SCOPUS:85175245472
T3 - Proceedings - 2023 3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023
SP - 74
EP - 79
BT - Proceedings - 2023 3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 19 June 2023 through 20 June 2023
ER -