Social Media Mining @ IPLab

2019

@Article{Ortis2019survey,
author  = {A. Ortis and G. M. Farinella and S. Battiato},
title   = {A Survey on Visual Sentiment Analysis},
journal = {IET Image Processing},
year    = {2019},
volume  = {},
pages   = {},
url     = {},
}
@Article{Ortis2019expliting,
author  = {A. Ortis and G. M. Farinella and G. Torrisi and S. Battiato},
title   = {Exploiting Objective Text Description of Images for Visual Sentiment Analysis},
journal = {Multimedia Tools and Applications},
year    = {2019},
volume  = {1},
pages   = {25--46},
url     = {},
}

2018

@INPROCEEDINGS{8516481,
author={A. Ortis and G. M. Farinella and G. Torrisi and S. Battiato},
booktitle={2018 International Conference on Content-Based Multimedia Indexing (CBMI)},
title={Visual Sentiment Analysis Based on on Objective Text Description of Images},
year={2018},
volume={},
number={},
pages={1-6},
abstract={Visual Sentiment Analysis aims to estimate the polarity of the sentiment evoked by images in terms of positive or negative sentiment. To this aim, most of the state of the art works exploit the text associated to a social post provided by the user. However, such textual data is typically noisy due to the subjectivity of the user which usually includes text useful to maximize the diffusion of the social post. In this paper we extract and employ an Objective Text description of images automatically extracted from the visual content rather than the classic Subjective Text provided by the users. The proposed method defines a multimodal embedding space based on the contribute of both visual and textual features. The sentiment polarity is then inferred by a supervised Support Vector Machine trained on the representations of the obtained embedding space. Experiments performed on a representative dataset of 47235 labelled samples demonstrate that the exploitation of the proposed Objective Text helps to outperform state-of-the-art for sentiment polarity estimation.},
keywords={data mining;image representation;learning (artificial intelligence);pattern classification;sentiment analysis;social networking (online);support vector machines;textual data;social post;visual features;textual features;sentiment polarity estimation;visual sentiment analysis;positive sentiment;negative sentiment;classic subjective text;objective text description images;multimodal embedding space;support vector machine;Visualization;Feature extraction;Sentiment analysis;Dogs;Flickr;Task analysis;Noise measurement;Visual Sentiment Analysis;Social Media Analysis;Objective Text Description;Multimodal Embedding},
doi={10.1109/CBMI.2018.8516481},
ISSN={},
month={Sept},
url={https://ieeexplore.ieee.org/abstract/document/8516481},}

2017

@Conference{Battiato201751,
author        = {Battiato, S. and Farinella, G.M. and Milotta, F.L.M. and Ortis, A. and Stanco, F. and D’Amico, V. and Addesso, L. and Torrisi, G.},
title         = {Organizing videos streams for clustering and estimation of popular scenes},
journal       = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
year          = {2017},
volume        = {10484 LNCS},
pages         = {51-61},
document_type = {Conference Paper},
doi           = {10.1007/978-3-319-68560-1_5},
source        = {Scopus},
url           = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032477500&doi=10.1007%2f978-3-319-68560-1_5&partnerID=40&md5=2c567fccad1cbba7154dd2de537fbc21},
}

2016

@Conference{Battiato2016397,
author        = {Battiato, S. and Farinella, G.M. and Milotta, F.L.M. and Ortis, A. and Addesso, L. and Casella, A. and D'Amico, V. and Torrisi, G.},
title         = {The social picture},
year          = {2016},
pages         = {397-400},
document_type = {Conference Paper},
doi           = {10.1145/2911996.2912024},
journal       = {ICMR 2016 - Proceedings of the 2016 ACM International Conference on Multimedia Retrieval},
source        = {Scopus},
url           = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84978767543&doi=10.1145%2f2911996.2912024&partnerID=40&md5=e2d54891ca51531f8070bf86ec3c51eb},
}
@Conference{Milotta2016,
author        = {Milotta, F.L.M. and Battiato, S. and Stanco, F. and D'amico, V. and Torrisi, G. and Addesso, L.},
title         = {RECfusion: Automatic scene clustering and tracking in videos from multiple sources},
year          = {2016},
volume        = {Part F129967},
document_type = {Conference Paper},
doi           = {10.2352/ISSN.2470-1173.2016.7MOBMU-292},
journal       = {IS and T International Symposium on Electronic Imaging Science and Technology},
source        = {Scopus},
url           = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032489624&doi=10.2352%2fISSN.2470-1173.2016.7MOBMU-292&partnerID=40&md5=f7c28408b19a48b93fed58a0ff36ab87},
}

2015

@InProceedings{Ortis2015a,
author    = {Ortis A and Farinella GM and D'Amico and V and Addesso L and Torrisi G and BATTIATO S.},
title     = {RECfusion: Automatic video curation driven by visual content popularity},
booktitle = {MM 2015 - Proceedings of the 2015 ACM Multimedia Conference},
year      = {2015},
pages     = {1179--1182},
address   = {New York -- USA},
publisher = {ACM Digital Library},
date      = {2015-01-01},
doi       = {10.1145/2733373.2806311},
pubstate  = {published},
tppubtype = {inproceedings},
url       = {http://dx.medra.org/10.1145/2733373.2806311},
}