Abstract
A lot of work has been attempted in the area of sentiment analysis (SA)/opinion mining of natural language texts (NLT) and social media. One of the major objectives of such tasks is to allocate polarity either positive (+ve) or negative (−ve) to a part of the text. But, at a similar time, the problem of assigning the degree of positivity and negativity of particular text occurs. The problem becomes more difficult in the case of text gathered from social sites, as these sites contain a number of emoticons and sarcasm words that have hidden meaning along with the expressions. In this paper, we have presented an emoticons and text sarcasm detection system. The value of the uploaded text document is generated by removing the stop words and data filtration process. Here, two types of polarities are identified, namely positive and negative for both sarcasm and emoticons with 100% accuracy. For classifying the polarities, artificial neural network (ANN) is used as a classifier. At last, the comparison between proposed work and existing work is discussed.
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Gupta, S., Singh, R., Singla, V. (2020). Emoticon and Text Sarcasm Detection in Sentiment Analysis. In: Luhach, A., Kosa, J., Poonia, R., Gao, XZ., Singh, D. (eds) First International Conference on Sustainable Technologies for Computational Intelligence. Advances in Intelligent Systems and Computing, vol 1045. Springer, Singapore. https://doi.org/10.1007/978-981-15-0029-9_1
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