AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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You can use the tag cloud to select only the papers dealing with specific research topics.
You can expand the Abstract, Links and BibTex record for each paper.
2015
Terrana, Diego; Augello, Agnese; Pilato, Giovanni
A System for Analysis and Comparison of Social Network Profiles Proceedings Article
In: M.S., Li T. Wang W. Kankanhalli (Ed.): Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing, IEEE ICSC 2015, pp. 109–115, Institute of Electrical and Electronics Engineers Inc., 2015, ISBN: 978-1-4799-7935-6.
Abstract | Links | BibTeX | Tags: Semantic Computing, User Profiling
@inproceedings{terranaSystemAnalysisComparison2015,
title = {A System for Analysis and Comparison of Social Network Profiles},
author = { Diego Terrana and Agnese Augello and Giovanni Pilato},
editor = { Li T. Wang W. Kankanhalli M.S.},
doi = {10.1109/ICOSC.2015.7050787},
isbn = {978-1-4799-7935-6},
year = {2015},
date = {2015-01-01},
booktitle = {Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing, IEEE ICSC 2015},
pages = {109--115},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {This work proposes a system for the analysis and the comparison of users profiles in social networks. Posts are extracted and analyzed in order to detect similar contents, like topics, sentiments and writing styles. A case study regarding the analysis of the authenticity of profiles of the Italian prime minister in different social networks is illustrated. textcopyright 2015 IEEE.},
keywords = {Semantic Computing, User Profiling},
pubstate = {published},
tppubtype = {inproceedings}
}
This work proposes a system for the analysis and the comparison of users profiles in social networks. Posts are extracted and analyzed in order to detect similar contents, like topics, sentiments and writing styles. A case study regarding the analysis of the authenticity of profiles of the Italian prime minister in different social networks is illustrated. textcopyright 2015 IEEE.
Terrana, Diego; Augello, Agnese; Pilato, Giovanni
A system for analysis and comparison of social network profiles Proceedings Article
In: M.S., Wang W. Li T. Kankanhalli (Ed.): Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing, IEEE ICSC 2015, pp. 109–115, Institute of Electrical and Electronics Engineers Inc., 2015, ISBN: 978-1-4799-7935-6.
Abstract | Links | BibTeX | Tags: Semantic Computing, User Profiling
@inproceedings{terrana_system_2015,
title = {A system for analysis and comparison of social network profiles},
author = {Diego Terrana and Agnese Augello and Giovanni Pilato},
editor = {Wang W. Li T. Kankanhalli M.S.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84925624640&doi=10.1109%2fICOSC.2015.7050787&partnerID=40&md5=9c0cc624ce85139c1fca57e14f61f8b6},
doi = {10.1109/ICOSC.2015.7050787},
isbn = {978-1-4799-7935-6},
year = {2015},
date = {2015-01-01},
booktitle = {Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing, IEEE ICSC 2015},
pages = {109–115},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {This work proposes a system for the analysis and the comparison of users profiles in social networks. Posts are extracted and analyzed in order to detect similar contents, like topics, sentiments and writing styles. A case study regarding the analysis of the authenticity of profiles of the Italian prime minister in different social networks is illustrated. © 2015 IEEE.},
keywords = {Semantic Computing, User Profiling},
pubstate = {published},
tppubtype = {inproceedings}
}
This work proposes a system for the analysis and the comparison of users profiles in social networks. Posts are extracted and analyzed in order to detect similar contents, like topics, sentiments and writing styles. A case study regarding the analysis of the authenticity of profiles of the Italian prime minister in different social networks is illustrated. © 2015 IEEE.
2014
Terrana, Diego; Augello, Agnese; Pilato, Giovanni
Analysis of Facebook Users' Relationships Through Sentiment Classification: A Case Study of Italian Politicians Journal Article
In: International Journal of Semantic Computing, vol. 8, no. 3, pp. 301–317, 2014, ISSN: 1793351X.
Abstract | Links | BibTeX | Tags: Facebook, Sentiment Analysis, User Profiling
@article{terranaAnalysisFacebookUsers2014,
title = {Analysis of Facebook Users' Relationships Through Sentiment Classification: A Case Study of Italian Politicians},
author = { Diego Terrana and Agnese Augello and Giovanni Pilato},
doi = {10.1142/S1793351X14400108},
issn = {1793351X},
year = {2014},
date = {2014-01-01},
journal = {International Journal of Semantic Computing},
volume = {8},
number = {3},
pages = {301--317},
abstract = {We illustrate a system that analyzes the homepage of a Facebook user or group in order to automatically detect who has discussed what and how it has been discussed. We have focused the analysis on three main actors of Italian politics. The goal is to find people who agree or disagree about given topics with the owner of the Facebook page under analysis. All public posts shared by a user are retrieved by an ad hoc built crawler. Information such as 'posts', 'comments', 'likes', are extracted from the Facebook page. Each post is classified as belonging to a set of predefined categories and its sentiment is also detected as being positive, negative or neutral. All the comments to that post are therefore analyzed and categorized together with its sentiment polarity. For each Facebook user under analysis a statistics of the topics dealt with is made, and for each category a graph is created where the concordance of sentiment is highlighted between the posts belonging to a given class and the related comments of the people interacting with the user or group under analysis. The graph can therefore be used to profile the user relationships according to sentiment classification. textcopyright 2014 World Scientific Publishing Company.},
keywords = {Facebook, Sentiment Analysis, User Profiling},
pubstate = {published},
tppubtype = {article}
}
We illustrate a system that analyzes the homepage of a Facebook user or group in order to automatically detect who has discussed what and how it has been discussed. We have focused the analysis on three main actors of Italian politics. The goal is to find people who agree or disagree about given topics with the owner of the Facebook page under analysis. All public posts shared by a user are retrieved by an ad hoc built crawler. Information such as 'posts', 'comments', 'likes', are extracted from the Facebook page. Each post is classified as belonging to a set of predefined categories and its sentiment is also detected as being positive, negative or neutral. All the comments to that post are therefore analyzed and categorized together with its sentiment polarity. For each Facebook user under analysis a statistics of the topics dealt with is made, and for each category a graph is created where the concordance of sentiment is highlighted between the posts belonging to a given class and the related comments of the people interacting with the user or group under analysis. The graph can therefore be used to profile the user relationships according to sentiment classification. textcopyright 2014 World Scientific Publishing Company.
Terrana, Diego; Augello, Agnese; Pilato, Giovanni
Facebook Users Relationships Analysis Based on Sentiment Classification Proceedings Article
In: Proceedings - 2014 IEEE International Conference on Semantic Computing, ICSC 2014, pp. 290–296, IEEE Computer Society, 2014, ISBN: 978-1-4799-4002-8.
Abstract | Links | BibTeX | Tags: Facebook, Semantic Computing, Sentiment Analysis, User Profiling
@inproceedings{terranaFacebookUsersRelationships2014,
title = {Facebook Users Relationships Analysis Based on Sentiment Classification},
author = { Diego Terrana and Agnese Augello and Giovanni Pilato},
doi = {10.1109/ICSC.2014.59},
isbn = {978-1-4799-4002-8},
year = {2014},
date = {2014-01-01},
booktitle = {Proceedings - 2014 IEEE International Conference on Semantic Computing, ICSC 2014},
pages = {290--296},
publisher = {IEEE Computer Society},
abstract = {It is presented an approach aimed at analyzing the homepage of a Facebook user or group in order to automatically detect who has discussed what and how it has been discussed. All public posts shared by an user are retrieved by an ad hoc built crawler. Information such as a text messages, comments, likes, is extracted for each post. Each post is classified as belonging to a set of predefined categories and its sentiment is also detected as being positive, negative or neutral. All the comments to that post are therefore analyzed and categorized together with its sentiment polarity. For each category it is created a graph where it is highlighted the concordance of sentiment between the posts and the related comments. The graph can be therefore used to profile the user relationships according to sentiment classification. textcopyright 2014 IEEE.},
keywords = {Facebook, Semantic Computing, Sentiment Analysis, User Profiling},
pubstate = {published},
tppubtype = {inproceedings}
}
It is presented an approach aimed at analyzing the homepage of a Facebook user or group in order to automatically detect who has discussed what and how it has been discussed. All public posts shared by an user are retrieved by an ad hoc built crawler. Information such as a text messages, comments, likes, is extracted for each post. Each post is classified as belonging to a set of predefined categories and its sentiment is also detected as being positive, negative or neutral. All the comments to that post are therefore analyzed and categorized together with its sentiment polarity. For each category it is created a graph where it is highlighted the concordance of sentiment between the posts and the related comments. The graph can be therefore used to profile the user relationships according to sentiment classification. textcopyright 2014 IEEE.
Terrana, Diego; Augello, Agnese; Pilato, Giovanni
Analysis of Facebook Users' Relationships Through Sentiment Classification: A Case Study of Italian Politicians Journal Article
In: International Journal of Semantic Computing, vol. 8, no. 3, pp. 301–317, 2014, ISSN: 1793351X.
Abstract | Links | BibTeX | Tags: Facebook, Sentiment Analysis, User Profiling
@article{terrana_analysis_2014,
title = {Analysis of Facebook Users' Relationships Through Sentiment Classification: A Case Study of Italian Politicians},
author = {Diego Terrana and Agnese Augello and Giovanni Pilato},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85051054194&doi=10.1142%2fS1793351X14400108&partnerID=40&md5=21a74c6a7fc4060d40ca34bf530f82d9},
doi = {10.1142/S1793351X14400108},
issn = {1793351X},
year = {2014},
date = {2014-01-01},
journal = {International Journal of Semantic Computing},
volume = {8},
number = {3},
pages = {301–317},
abstract = {We illustrate a system that analyzes the homepage of a Facebook user or group in order to automatically detect who has discussed what and how it has been discussed. We have focused the analysis on three main actors of Italian politics. The goal is to find people who agree or disagree about given topics with the owner of the Facebook page under analysis. All public posts shared by a user are retrieved by an ad hoc built crawler. Information such as 'posts', 'comments', 'likes', are extracted from the Facebook page. Each post is classified as belonging to a set of predefined categories and its sentiment is also detected as being positive, negative or neutral. All the comments to that post are therefore analyzed and categorized together with its sentiment polarity. For each Facebook user under analysis a statistics of the topics dealt with is made, and for each category a graph is created where the concordance of sentiment is highlighted between the posts belonging to a given class and the related comments of the people interacting with the user or group under analysis. The graph can therefore be used to profile the user relationships according to sentiment classification. © 2014 World Scientific Publishing Company.},
keywords = {Facebook, Sentiment Analysis, User Profiling},
pubstate = {published},
tppubtype = {article}
}
We illustrate a system that analyzes the homepage of a Facebook user or group in order to automatically detect who has discussed what and how it has been discussed. We have focused the analysis on three main actors of Italian politics. The goal is to find people who agree or disagree about given topics with the owner of the Facebook page under analysis. All public posts shared by a user are retrieved by an ad hoc built crawler. Information such as 'posts', 'comments', 'likes', are extracted from the Facebook page. Each post is classified as belonging to a set of predefined categories and its sentiment is also detected as being positive, negative or neutral. All the comments to that post are therefore analyzed and categorized together with its sentiment polarity. For each Facebook user under analysis a statistics of the topics dealt with is made, and for each category a graph is created where the concordance of sentiment is highlighted between the posts belonging to a given class and the related comments of the people interacting with the user or group under analysis. The graph can therefore be used to profile the user relationships according to sentiment classification. © 2014 World Scientific Publishing Company.
Terrana, Diego; Augello, Agnese; Pilato, Giovanni
Facebook users relationships analysis based on sentiment classification Proceedings Article
In: Proceedings - 2014 IEEE International Conference on Semantic Computing, ICSC 2014, pp. 290–296, IEEE Computer Society, 2014, ISBN: 978-1-4799-4002-8.
Abstract | Links | BibTeX | Tags: Facebook, Semantic Computing, Sentiment Analysis, User Profiling
@inproceedings{terrana_facebook_2014,
title = {Facebook users relationships analysis based on sentiment classification},
author = {Diego Terrana and Agnese Augello and Giovanni Pilato},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84906971661&doi=10.1109%2fICSC.2014.59&partnerID=40&md5=f092893c5b61a78e0e7af00e7909ef30},
doi = {10.1109/ICSC.2014.59},
isbn = {978-1-4799-4002-8},
year = {2014},
date = {2014-01-01},
booktitle = {Proceedings - 2014 IEEE International Conference on Semantic Computing, ICSC 2014},
pages = {290–296},
publisher = {IEEE Computer Society},
abstract = {It is presented an approach aimed at analyzing the homepage of a Facebook user or group in order to automatically detect who has discussed what and how it has been discussed. All public posts shared by an user are retrieved by an ad hoc built crawler. Information such as a text messages, comments, likes, is extracted for each post. Each post is classified as belonging to a set of predefined categories and its sentiment is also detected as being positive, negative or neutral. All the comments to that post are therefore analyzed and categorized together with its sentiment polarity. For each category it is created a graph where it is highlighted the concordance of sentiment between the posts and the related comments. The graph can be therefore used to profile the user relationships according to sentiment classification. © 2014 IEEE.},
keywords = {Facebook, Semantic Computing, Sentiment Analysis, User Profiling},
pubstate = {published},
tppubtype = {inproceedings}
}
It is presented an approach aimed at analyzing the homepage of a Facebook user or group in order to automatically detect who has discussed what and how it has been discussed. All public posts shared by an user are retrieved by an ad hoc built crawler. Information such as a text messages, comments, likes, is extracted for each post. Each post is classified as belonging to a set of predefined categories and its sentiment is also detected as being positive, negative or neutral. All the comments to that post are therefore analyzed and categorized together with its sentiment polarity. For each category it is created a graph where it is highlighted the concordance of sentiment between the posts and the related comments. The graph can be therefore used to profile the user relationships according to sentiment classification. © 2014 IEEE.
2011
Augello, Agnese; Ortolani, Marco; Re, Giuseppe Lo; Gaglio, Salvatore
Sensor Mining for User Behavior Profiling in Intelligent Environments Journal Article
In: Studies in Computational Intelligence, vol. 361, pp. 143–158, 2011, ISSN: 1860949X.
Abstract | Links | BibTeX | Tags: Intelligent Environment, User Profiling
@article{augelloSensorMiningUser2011,
title = {Sensor Mining for User Behavior Profiling in Intelligent Environments},
author = { Agnese Augello and Marco Ortolani and Giuseppe Lo Re and Salvatore Gaglio},
editor = { Soro A. Vargiu E. Pallotta V.},
doi = {10.1007/978-3-642-21384-7_10},
issn = {1860949X},
year = {2011},
date = {2011-01-01},
journal = {Studies in Computational Intelligence},
volume = {361},
pages = {143--158},
abstract = {The proposed system exploits sensor mining methodologies to profile user behaviors patterns in an intelligent workplace. The work is based in the assumption that users' habit profiles are implicitly described by sensory data, which explicitly show the consequences of users' actions over the environment state. Sensor data are analyzed in order to infer relationships of interest between environmental variables and the user, detecting in this way behavior profiles. The system is designed for a workplace equipped in the context of Sensor9k, a project carried out at the Department of Computer Science of Palermo University. textcopyright 2011 Springer-Verlag Berlin Heidelberg.},
keywords = {Intelligent Environment, User Profiling},
pubstate = {published},
tppubtype = {article}
}
The proposed system exploits sensor mining methodologies to profile user behaviors patterns in an intelligent workplace. The work is based in the assumption that users' habit profiles are implicitly described by sensory data, which explicitly show the consequences of users' actions over the environment state. Sensor data are analyzed in order to infer relationships of interest between environmental variables and the user, detecting in this way behavior profiles. The system is designed for a workplace equipped in the context of Sensor9k, a project carried out at the Department of Computer Science of Palermo University. textcopyright 2011 Springer-Verlag Berlin Heidelberg.
Augello, Agnese; Ortolani, Marco; Re, Giuseppe Lo; Gaglio, Salvatore
Sensor mining for user behavior profiling in intelligent environments Journal Article
In: Studies in Computational Intelligence, vol. 361, pp. 143–158, 2011, ISSN: 1860949X.
Abstract | Links | BibTeX | Tags: Intelligent Environment, User Profiling
@article{augello_sensor_2011,
title = {Sensor mining for user behavior profiling in intelligent environments},
author = {Agnese Augello and Marco Ortolani and Giuseppe Lo Re and Salvatore Gaglio},
editor = {Vargiu E. Soro A. Pallotta V.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-79961072111&doi=10.1007%2f978-3-642-21384-7_10&partnerID=40&md5=cbc2662d7b48999129237e4163aa07b1},
doi = {10.1007/978-3-642-21384-7_10},
issn = {1860949X},
year = {2011},
date = {2011-01-01},
journal = {Studies in Computational Intelligence},
volume = {361},
pages = {143–158},
abstract = {The proposed system exploits sensor mining methodologies to profile user behaviors patterns in an intelligent workplace. The work is based in the assumption that users' habit profiles are implicitly described by sensory data, which explicitly show the consequences of users' actions over the environment state. Sensor data are analyzed in order to infer relationships of interest between environmental variables and the user, detecting in this way behavior profiles. The system is designed for a workplace equipped in the context of Sensor9k, a project carried out at the Department of Computer Science of Palermo University. © 2011 Springer-Verlag Berlin Heidelberg.},
keywords = {Intelligent Environment, User Profiling},
pubstate = {published},
tppubtype = {article}
}
The proposed system exploits sensor mining methodologies to profile user behaviors patterns in an intelligent workplace. The work is based in the assumption that users' habit profiles are implicitly described by sensory data, which explicitly show the consequences of users' actions over the environment state. Sensor data are analyzed in order to infer relationships of interest between environmental variables and the user, detecting in this way behavior profiles. The system is designed for a workplace equipped in the context of Sensor9k, a project carried out at the Department of Computer Science of Palermo University. © 2011 Springer-Verlag Berlin Heidelberg.