Sarcasm detection with glove and Word2Vec models
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Author |
Raghava Prasad, N. Adithya Reddy, Ruthvik Varma G. and Mohammed Shuaib
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e-ISSN |
1819-6608 |
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On Pages
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1181-1186
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Volume No. |
18
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Issue No. |
10
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Issue Date |
July 25, 2023
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DOI |
https://doi.org/10.59018/0523154
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Keywords |
sarcasm detection, machine learning, sentimental analysis, GloVe model, word2Vec model.
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Abstract
In subtle communication, such as sarcasm, the speaker expresses the antithesis of what is indicated. The ambiguity of sarcasm is one of the biggest obstacles to its detection. Satirical language is not specifically defined. The increasing number of languages is a significant problem as well. On these websites, a large number of new slang phrases are developed each day. Therefore, it is possible that sarcasm cannot be accurately detected using the current corpus of positive and negative feelings. Additionally, users are now able to employ a variety of emoticons with text thanks to recent improvements in online social networks. The sentence could become sarcastic by using these emoticons, which can flip its polarity. Sentiment analysis' accuracy can be increased by carefully analysing and comprehending sarcastic statements. Sentiment analysis is the process of determining how people feel or what they think about a certain situation or issue and detection of sarcasm has become a part of it. A two-phase structure is used for the study article. Following the implementation of two models, GloVe and Word2Vec, we came to the conclusion as to which model is more effective at detecting sarcasm and may be used in real-time applications. The first step of the algorithm gathers features linked to moods and punctuation. In the first technique, the Word2Vec model achieves accuracy of 79.38%, and in the second way, the GloVe model achieves accuracy of 82.33%.
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