The Experts below are selected from a list of 2340 Experts worldwide ranked by ideXlab platform
Armando Roy - One of the best experts on this subject based on the ideXlab platform.
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Temporal Orientation panel for special education
Lecture Notes in Computer Science, 2006Co-Authors: Jorge L. Falcó, Carmen Muro, Inmaculada Plaza, Armando RoyAbstract:In the present work an electronic panel designed for helping people with deficiencies in their understanding of the sense of time is showed. The device will be installed in special education classrooms. It tries to meet the following objectives: 1) To provide disabled people with Temporal Orientation and to help in learning the use of conventional clocks. 2) To make the concept of Temporal grouping easier. The grouping mechanism reflects the ability to use the information about task or work at some point correlated with Temporal indicators. 3) To make anticipation of the sequence of events possible for autistic children. The results of the device's evaluation'will allow authors to improve the design. Thus, it could be possible to extrapolate the use of the device on homes for disabled children or for elderly people with cognitive disabilities or even in the early stages of senile dementia.
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ICCHP - Temporal Orientation panel for special education
Lecture Notes in Computer Science, 2006Co-Authors: Jorge L. Falcó, Carmen Muro, Inmaculada Plaza, Armando RoyAbstract:In the present work an electronic panel designed for helping people with deficiencies in their understanding of the sense of time is showed. The device will be installed in special education classrooms. It tries to meet the following objectives: 1) To provide disabled people with Temporal Orientation and to help in learning the use of conventional clocks. 2) To make the concept of Temporal grouping easier. The grouping mechanism reflects the ability to use the information about task or work at some point correlated with Temporal indicators. 3) To make anticipation of the sequence of events possible for autistic children. The results of the device's evaluation will allow authors to improve the design. Thus, it could be possible to extrapolate the use of the device on homes for disabled children or for elderly people with cognitive disabilities or even in the early stages of senile dementia
Jorge L. Falcó - One of the best experts on this subject based on the ideXlab platform.
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Temporal Orientation panel for special education
Lecture Notes in Computer Science, 2006Co-Authors: Jorge L. Falcó, Carmen Muro, Inmaculada Plaza, Armando RoyAbstract:In the present work an electronic panel designed for helping people with deficiencies in their understanding of the sense of time is showed. The device will be installed in special education classrooms. It tries to meet the following objectives: 1) To provide disabled people with Temporal Orientation and to help in learning the use of conventional clocks. 2) To make the concept of Temporal grouping easier. The grouping mechanism reflects the ability to use the information about task or work at some point correlated with Temporal indicators. 3) To make anticipation of the sequence of events possible for autistic children. The results of the device's evaluation'will allow authors to improve the design. Thus, it could be possible to extrapolate the use of the device on homes for disabled children or for elderly people with cognitive disabilities or even in the early stages of senile dementia.
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ICCHP - Temporal Orientation panel for special education
Lecture Notes in Computer Science, 2006Co-Authors: Jorge L. Falcó, Carmen Muro, Inmaculada Plaza, Armando RoyAbstract:In the present work an electronic panel designed for helping people with deficiencies in their understanding of the sense of time is showed. The device will be installed in special education classrooms. It tries to meet the following objectives: 1) To provide disabled people with Temporal Orientation and to help in learning the use of conventional clocks. 2) To make the concept of Temporal grouping easier. The grouping mechanism reflects the ability to use the information about task or work at some point correlated with Temporal indicators. 3) To make anticipation of the sequence of events possible for autistic children. The results of the device's evaluation will allow authors to improve the design. Thus, it could be possible to extrapolate the use of the device on homes for disabled children or for elderly people with cognitive disabilities or even in the early stages of senile dementia
Sabyasachi Kamila - One of the best experts on this subject based on the ideXlab platform.
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Resolution of grammatical tense into actual time, and its application in Time Perspective study in the tweet space.
PloS one, 2019Co-Authors: Sabyasachi Kamila, Asif Ekbal, Mohammad Hasanuzzaman, Pushpak BhattacharyyaAbstract: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.
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fine grained Temporal Orientation and its relationship with psycho demographic correlates
North American Chapter of the Association for Computational Linguistics, 2018Co-Authors: Sabyasachi Kamila, Mohammed Hasanuzzaman, Asif Ekbal, Pushpak Bhattacharyya, Andy WayAbstract: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’ psychodemographic 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 psychodemographic factors using regression.
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NAACL-HLT - Fine-Grained Temporal Orientation and its Relationship with Psycho-Demographic Correlates
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies Volume , 2018Co-Authors: Sabyasachi Kamila, Mohammed Hasanuzzaman, Asif Ekbal, Pushpak Bhattacharyya, Andy WayAbstract: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’ psychodemographic 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 psychodemographic factors using regression.
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Temporal Orientation of tweets for predicting income of users
Meeting of the Association for Computational Linguistics, 2017Co-Authors: Mohammed Hasanuzzaman, Sabyasachi Kamila, Mandeep Kaur, Sriparna Saha, Asif EkbalAbstract:Automatically estimating a user’s socioeconomic 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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ACL (2) - Temporal Orientation of Tweets for Predicting Income of Users
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 2017Co-Authors: Mohammed Hasanuzzaman, Sabyasachi Kamila, Mandeep Kaur, Sriparna Saha, Asif EkbalAbstract:Automatically estimating a user’s socioeconomic 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.
Asif Ekbal - One of the best experts on this subject based on the ideXlab platform.
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Resolution of grammatical tense into actual time, and its application in Time Perspective study in the tweet space.
PloS one, 2019Co-Authors: Sabyasachi Kamila, Asif Ekbal, Mohammad Hasanuzzaman, Pushpak BhattacharyyaAbstract: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.
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fine grained Temporal Orientation and its relationship with psycho demographic correlates
North American Chapter of the Association for Computational Linguistics, 2018Co-Authors: Sabyasachi Kamila, Mohammed Hasanuzzaman, Asif Ekbal, Pushpak Bhattacharyya, Andy WayAbstract: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’ psychodemographic 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 psychodemographic factors using regression.
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NAACL-HLT - Fine-Grained Temporal Orientation and its Relationship with Psycho-Demographic Correlates
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies Volume , 2018Co-Authors: Sabyasachi Kamila, Mohammed Hasanuzzaman, Asif Ekbal, Pushpak Bhattacharyya, Andy WayAbstract: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’ psychodemographic 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 psychodemographic factors using regression.
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Temporal Orientation of tweets for predicting income of users
Meeting of the Association for Computational Linguistics, 2017Co-Authors: Mohammed Hasanuzzaman, Sabyasachi Kamila, Mandeep Kaur, Sriparna Saha, Asif EkbalAbstract:Automatically estimating a user’s socioeconomic 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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ACL (2) - Temporal Orientation of Tweets for Predicting Income of Users
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 2017Co-Authors: Mohammed Hasanuzzaman, Sabyasachi Kamila, Mandeep Kaur, Sriparna Saha, Asif EkbalAbstract:Automatically estimating a user’s socioeconomic 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.
Pushpak Bhattacharyya - One of the best experts on this subject based on the ideXlab platform.
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Resolution of grammatical tense into actual time, and its application in Time Perspective study in the tweet space.
PloS one, 2019Co-Authors: Sabyasachi Kamila, Asif Ekbal, Mohammad Hasanuzzaman, Pushpak BhattacharyyaAbstract: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.
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fine grained Temporal Orientation and its relationship with psycho demographic correlates
North American Chapter of the Association for Computational Linguistics, 2018Co-Authors: Sabyasachi Kamila, Mohammed Hasanuzzaman, Asif Ekbal, Pushpak Bhattacharyya, Andy WayAbstract: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’ psychodemographic 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 psychodemographic factors using regression.
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NAACL-HLT - Fine-Grained Temporal Orientation and its Relationship with Psycho-Demographic Correlates
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies Volume , 2018Co-Authors: Sabyasachi Kamila, Mohammed Hasanuzzaman, Asif Ekbal, Pushpak Bhattacharyya, Andy WayAbstract: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’ psychodemographic 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 psychodemographic factors using regression.
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Temporality as seen through translation:a case study on Hindi texts
2017Co-Authors: Sabyasachi Kamila, Mohammed Hasanuzzaman, Asif Ekbal, Andy Way, Sukanta Sen, Pushpak BhattacharyyaAbstract:Temporality has significantly contributed to various aspects of Natural Language Processing applications. In this paper, we determine the extent to which Temporal Orientation is preserved when a sentence is translated manually and automatically from the Hindi language to the English language. We show that the manually and automatically identified Temporal Orientation in English translated (both manual and automatic) sentences provides a good match with the Temporal Orientation of the Hindi texts. We also find that the task of manual Temporal annotation becomes difficult in the translated texts while the automatic Temporal processing system manages to correctly capture Temporal information from the translations.
