Publication Type:

Conference Paper

Source:

International Conference on Computational Intelligence and Data Science (ICCIDS 2018)., Volume 132, p.1646-1653 (2018)

URL:

https://www.sciencedirect.com/science/article/pii/S1877050918308640

Keywords:

Bidirectional, Deep learning, Gated Recurrent Unit (GRU), LSTM, RNN, Stance Classification, Word2vec

Abstract:

Understanding the user intention from text is a problem of growing interest. The social media like Twitter, Facebook etc. extract user intention to analyze the behaviour of a user which in turn is employed for bot recognition, satire detection, fake news detection etc.. The process of identifying stance of a user from the text is called stance detection. This article compares the headline and body pair of a news article and classifies the pair as related or unrelated. The related pair is further classified into agree, disagree, discuss. We call related as detailed classification and unrelated as broad classification. We employ deep neural nets for feature extraction and stance classification. RNN models and its extensions showed significant variations in the classification of detailed class. Bidirectional LSTM model achieved the best accuracy for broad as well as detailed classification.

Cite this Research Publication

G. Rajendran, Dr. Bhadrachalam Chitturi, and Poornachandran, P., “Stance-In-Depth Deep Neural Approach to Stance Classification”, in International Conference on Computational Intelligence and Data Science (ICCIDS 2018)., 2018, vol. 132, pp. 1646-1653.