Temporal analysis of sentiment events: a visual realization and tracking

  • Authors:

  • Dipankar Das;Anup Kumar Kolya;Asif Ekbal;Sivaji Bandyopadhyay

  • Affiliations:

  • Department of Computer Science and Engineering, Jadavpur University, Kolkata, India;Department of Computer Science and Engineering, Jadavpur University, Kolkata, India;Department of Information Engineering and Computer Science, University of Trento, Italy;Department of Computer Science and Engineering, Jadavpur University, Kolkata, India

  • Venue:
  • CICLing'11 Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part I
  • Year:
  • 2011

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Abstract

In recent years, extraction of temporal relations for events that express sentiments has drawn great attention of the Natural Language Processing (NLP) research communities. In this work, we propose a method that involves the association and contribution of sentiments in determining the event-event relations from texts. Firstly, we employ a machine learning approach based on Conditional Random Field (CRF) for solving the problem of Task C (identification of event-event relations) of TempEval-2007 within TimeML framework by considering sentiment as a feature of an event. Incorporating sentiment property, our system achieves the performance that is better than all the participated state-of-the-art systems of TempEval 2007. Evaluation results on the Task C test set yield the F-score values of 57.2% under the strict evaluation scheme and 58.6% under the relaxed evaluation scheme. The positive or negative coarse grained sentiments as well as the Ekman's six basic universal emotions (or, fine grained sentiments) are assigned to the events. Thereafter, we analyze the temporal relations between events in order to track the sentiment events. Representation of the temporal relations in a graph format shows the shallow visual realization path for tracking the sentiments over events. Manual evaluation of temporal relations of sentiment events identified in 20 documents sounds satisfactory from the purview of event-sentiment tracking.