SARCASM DETECTION IN REDDIT COMMENTS
DOI:
https://doi.org/10.24867/07BE25PericKeywords:
Sarkasm, Convolutinal and Recurrent neural networks, Support Vector Machines, Logistic Regression, Random ForestAbstract
Sentiment analysis is widespread in research today, and sarcasm represents one of its problems, which can often lead the model to conclude the opposite of the correct sentiment. This phenomenon occurs because of the very nature of sarcasm: the use of positive words to express negative feelings. Therefore, the topic of sarcasm detection was chosen particularly in comments, where it occurs more often than in usual textual content. This paper presents different approaches to solving a given problem. Different classification models are proposed - Random Forest, Support Vector Machines (SVM), Logistic Regression, as well as various neural network architectures - Yoon Kim model, convolution model, recurrent model, and convolution-recurrent model. Along with related research, this work has shown that sarcasm detection is possible and that by further refining of the model, accuracy can be increased and thus contribute to a significant improvement in sentiment analysis.
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