Abstract
Sarcasm is usually used by people to either tease/irritate others or simply for comic purposes. The presence of sarcasm becomes certain as it is difficult to be identified by basic sentiment analysis method. Sarcasm detection is addressed with various rule-based methods, statistical approaches, and classifiers in machine learning , most of these are introduced to identify sarcasm in text written in English as it is a popular language on the internet. Although the groundwork done on sarcasm detection on various Indian languages like Telugu is limited. Hence, this paper presents a Deep learning model based on neural networks to detect sarcasm in Telugu news headlines taken from various websites . The proposed model comprises of Convolutional Neural Networks(CNN) and next a Long short-term memory(LSTM) Network which is a modified version of Recurrent neural networks (RNN) and lastly a fully connected dense layer is added to classify the sentiments into sarcastic and non-sarcastic. A pre-trained word embeddings GloVe are used in the model
Highlights
Sarcasm Detection is one of the research field in Natural language processing (NLP), a distinct case which is part of sentiment analysis where rather than identifying the sentence in the text as positive or negative sentiment, the emphasis is on sarcasm
Poria et al [7] proposed models which depend on pre-trained convolutional neural network for extracting features for sarcasm detection
In our experiment we have taken a dataset of 4000 plus sarcastic and non-sarcastic sentences written in Telugu language and applied the deep learning techniques for feature extraction and classification
Summary
Sarcasm Detection is one of the research field in Natural language processing (NLP), a distinct case which is part of sentiment analysis where rather than identifying the sentence in the text as positive or negative sentiment, the emphasis is on sarcasm. It is seen that in the above example the word ‘love’ implies a positive sentiment, but the situation in the example is negative as “nobody likes to wait for long hours”. This implies that the above example is sarcastic. With very few tools and annotated corpora available, it is a challenging task to perform sarcasm detection for Telugu language
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