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Applicant:
Indian Institute of Technology (IIT) Patna 
Author:
S. Kamila, M. Hasanuzzaman, A. Ekbal and P. Bhattacharyya  
Corresponding Authors:
S. Kamila, M. Hasanuzzaman 
DOI #:
Doi https://doi.org/10.1371/journal.pone.0211872 
Title:
Resolution of grammatical tense into actual time, and its application in Time Perspective study in the tweet space.  
Journal:
PLOS ONE 
Year:
2019 
Volume:
NA 
Page:
NA 
Keywords:
tweet space 
Abstract:
Time Perspective (TP) is an important area of research within the ‘psychological time’ paradigm. TP, or the manner in which individuals conduct themselves as a reflection of their cogitation of the past, the present, and the future, is considered as a basic facet of human functioning. These perceptions of time have an influence on our actions, perceptions, and emotions. Assessment of TP based on human language on Twitter opens up a new avenue for research on subjective view of time at a large scale. In order to assess TP of users’ from their tweets, the foremost task is to resolve grammatical tense into the underlying temporal orientation of tweets as for many tweets the tense information, and their temporal orientations are not the same. In this article, we first resolve grammatical tense of users’ tweets to identify their underlying temporal orientation: past, present, or future. We develop a minimally supervised classification framework for temporal orientation task that enables incorporating linguistic knowledge into a deep neural network. The temporal orientation model achieves an accuracy of 78.7% when tested on a manually annotated test set. This method performs better when compared to the state-of-the-art technique. Secondly, we apply the classification model to classify the users’ tweets in either of the past, present or future categories. Tweets classified this way are then grouped for each user which gives rise to unidimensional TP. The valence (positive, negative, and neutral) is added to the temporal orientation dimension to produce the bidimensional TP. We finally investigate the association between the Twitter users’ unidimensional and bidimensional TP and their age, education and six basic emotions in a large-scale empirical manner. Our analysis shows that people tend to think more about the past as well as more positive about the future when they age. We also observe that future-negative people are less joyful, more sad, more disgusted, and more angry while past-negative people have more fear. 
Entered by:
Venkata Dantham on 2020-08-04 
 
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