Sabyasachi Kamila


2018

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Fine-Grained Temporal Orientation and its Relationship with Psycho-Demographic Correlates
Sabyasachi Kamila | Mohammed Hasanuzzaman | Asif Ekbal | Pushpak Bhattacharyya | Andy Way
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

Temporal orientation refers to an individual’s tendency to connect to the psychological concepts of past, present or future, and it affects personality, motivation, emotion, decision making and stress coping processes. The study of the social media users’ psycho-demographic attributes from the perspective of human temporal orientation can be of utmost interest and importance to the business and administrative decision makers as it can provide an extra precious information for them to make informed decisions. In this paper, we propose a very first study to demonstrate the association between the sentiment view of the temporal orientation of the users and their different psycho-demographic attributes by analyzing their tweets. We first create a temporal orientation classifier in a minimally supervised way which classifies each tweet of the users in one of the three temporal categories, namely past, present, and future. A deep Bi-directional Long Short Term Memory (BLSTM) is used for the tweet classification task. Our tweet classifier achieves an accuracy of 78.27% when tested on a manually created test set. We then determine the users’ overall temporal orientation based on their tweets on the social media. The sentiment is added to the tweets at the fine-grained level where each temporal tweet is given a sentiment with either of the positive, negative or neutral. Our experiment reveals that depending upon the sentiment view of temporal orientation, a user’s attributes vary. We finally measure the correlation between the users’ sentiment view of temporal orientation and their different psycho-demographic factors using regression.

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Sentence Level Temporality Detection using an Implicit Time-sensed Resource
Sabyasachi Kamila | Asif Ekbal | Pushpak Bhattacharyya
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2017

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Temporal Orientation of Tweets for Predicting Income of Users
Mohammed Hasanuzzaman | Sabyasachi Kamila | Mandeep Kaur | Sriparna Saha | Asif Ekbal
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Automatically estimating a user’s socio-economic profile from their language use in social media can significantly help social science research and various downstream applications ranging from business to politics. The current paper presents the first study where user cognitive structure is used to build a predictive model of income. In particular, we first develop a classifier using a weakly supervised learning framework to automatically time-tag tweets as past, present, or future. We quantify a user’s overall temporal orientation based on their distribution of tweets, and use it to build a predictive model of income. Our analysis uncovers a correlation between future temporal orientation and income. Finally, we measure the predictive power of future temporal orientation on income by performing regression.

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Temporality as Seen through Translation: A Case Study on Hindi Texts
Sabyasachi Kamila | Sukanta Sen | Mohammad Hasanuzzaman | Asif Ekbal | Andy Way | Pushpak Bhattacharyya
Proceedings of Machine Translation Summit XVI: Research Track

2016

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Improving Document Ranking using Query Expansion and Classification Techniques for Mixed Script Information Retrieval
Subham Kumar | Anwesh Sinha Ray | Sabyasachi Kamila | Asif Ekbal | Sriparna Saha | Pushpak Bhattacharyya
Proceedings of the 13th International Conference on Natural Language Processing