Publications by Aniruddha Ghosh
Sarcasm is a pervasive phenomenon in social media, permitting the concise communication of meanin... more Sarcasm is a pervasive phenomenon in social media, permitting the concise communication of meaning, affect and attitude. Concision requires wit to produce and wit to understand, which demands from each party knowledge of norms, context and a speaker's mindset. Insight into a speaker's psychological profile at the time of production is a valuable source of context for sarcasm detection. Using a neural architecture , we show significant gains in detection accuracy when knowledge of the speaker's mood at the time of production can be inferred. Our focus is on sarcasm detection on Twitter, and show that the mood exhibited by a speaker over tweets leading up to a new post is as useful a cue for sarcasm as the topical context of the post itself. The work opens the door to an empirical exploration not just of sarcasm in text but of the sarcastic state of mind.
Bookmarks Related papers MentionsView impact
Precise semantic representation of a sentence and definitive information extraction are key steps... more Precise semantic representation of a sentence and definitive information extraction are key steps in the accurate processing of sentence meaning, especially for figurative phenomena such as sarcasm, Irony, and metaphor cause literal meanings to be discounted and secondary or extended meanings to be intentionally profiled. Semantic modelling faces a new challenge in social media, because grammatical inaccuracy is commonplace yet many previous state-of-the-art methods exploit grammatical structure. For sarcasm detection over social media content, researchers so far have counted on Bag-of-Words(BOW), N-grams etc. In this paper, we propose a neural network semantic model for the task of sarcasm detection. We also review semantic modelling using Support Vector Machine (SVM) that employs constituency parse-trees fed and labeled with syntactic and semantic information. The proposed neural network model composed of Convolution Neu-ral Network(CNN) and followed by a Long short term memory (LSTM) network and finally a Deep neural network(DNN). The proposed model outperforms state-of-the-art text-based methods for sarcasm detection, yielding an F-score of .92.
Bookmarks Related papers MentionsView impact
Papers by Aniruddha Ghosh
Bookmarks Related papers MentionsView impact
Proceedings of The 12th International Workshop on Semantic Evaluation, 2018
Bookmarks Related papers MentionsView impact
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, 2017
Bookmarks Related papers MentionsView impact
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), 2015
Bookmarks Related papers MentionsView impact
Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, 2016
Bookmarks Related papers MentionsView impact
Bookmarks Related papers MentionsView impact
Bookmarks Related papers MentionsView impact
Bookmarks Related papers MentionsView impact
Proceedings of the 13th European Workshop on Natural Language Generation, Sep 28, 2011
Bookmarks Related papers MentionsView impact
Bookmarks Related papers MentionsView impact
Bookmarks Related papers MentionsView impact
Bengali Parsing System at ICON NLP Tool Contest 2010 Aniruddha Ghosh Amitava Das* Pinaki Bhaskar... more Bengali Parsing System at ICON NLP Tool Contest 2010 Aniruddha Ghosh Amitava Das* Pinaki Bhaskar+ Sivaji Bandyopadhyay¥ Department of Computer Science and Engineering Jadavpur University Jadavpur, Kolkata 700032, India arghyaonline@ gmail. com amitava. santu@ ...
Bookmarks Related papers MentionsView impact
Uploads
Publications by Aniruddha Ghosh
Papers by Aniruddha Ghosh