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Identification of Sarcasm in Textual Data: A Comparative Study

Journal of Data and Information Science 2019年第4期4卷 56-83页

作者： Pulkit Mehndiratta devpriya soni Jaypee Institute of Information Technology

Purpose:Ever increasing penetration of the Internet in our lives has led to an enormous amount of multimedia content generation on the *** data contributes a major share towards data generated on the world wide *** people’s sentiment is an important aspect of natural language processing,but this opinion can be biased and incorrect,if people use sarcasm while commenting,posting status updates or reviewing any product or a ***,it is of utmost importance to detect sarcasm correctly and make a correct prediction about the people’s ***/methodology/approach:This study tries to evaluate various machine learning models along with standard and hybrid deep learning models across various standardized *** have performed vectorization of text using word embedding *** has been done to convert the textual data into vectors for analytical *** have used three standardized datasets available in public domain and used three word embeddings i.e Word2Vec,GloVe and fastText to validate the ***:The results were analyzed and conclusions are *** key finding is:the hybrid models that include Bidirectional LongTerm Short Memory(Bi-LSTM)and Convolutional Neural Network(CNN)outperform others conventional machine learning as well as deep learning models across all the datasets considered in this study,making our hypothesis *** limitations:Using the data from different sources and customizing the models according to each dataset,slightly decreases the usability of the ***,overall this methodology provides effective measures to identify the presence of sarcasm with a minimum average accuracy of 80%or above for one dataset and better than the current baseline results for the other *** implications:The results provide solid insights for the system developers to integrate this model into real-time analysis of any review or comment posted in the public *** study

关键词： Machine learning Artificial neural networks Word embedding Text vectorization Accuracy

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