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Extracting Protests from News Using LSTM models with different Attention Mechanisms

Extracting Protests from news is very useful because it helps in the early identification and subduing of contentious events and controlling violent public outbreaks. This also helps in the study of social sciences on the difference in protest types and their expression across countries. In this paper, a deep learning approach is presented for classifying input documents developed using data from one country and is exercised on data from others. Long Short-Term Memory(LSTM) a variant of Recurrent Neural Network(RNN) is used to implement our approach for the text classification. Models were created for 2 tasks in specific, the first aimed at identifying if a given news document led to a protest or not while the latter predicted if or not an input sentence contained an event trigger. The data given by CodaLab CLEF 2019 Lab ProtestNews was used to develop and evaluate the performance of the models. The documents and sentences were assigned labels ‘protest’/‘non-protest’ and ‘event-trigger’/‘not-event-trigger’. A total of 4 LSTM models were implemented with different attention mechanisms, and the ones implementing Scaled-Luong and Bahdanau obtained higher overall F1 macro scores of 0.3290 and 0.3682 on the final evaluation test set respectively.