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 expand the Abstract, Links and BibTex record for each paper.
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.
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.