Publications

@inproceedings{sangiorgiApproachDetectPolarity2014,

title = {An Approach to Detect Polarity Variation Rules for Sentiment Analysis},

author = { Pierluca Sangiorgi and Agnese Augello and Giovanni Pilato},

doi = {10.5220/0004961903440349},

isbn = {978-989-758-024-6},

year  = {2014},

date = {2014-01-01},

booktitle = {WEBIST 2014 - Proceedings of the 10th International Conference on Web Information Systems and Technologies},

volume = {2},

pages = {344--349},

publisher = {SciTePress},

abstract = {Sentiment Analysis is a discipline that aims at identifying and extract the subjectivity expressed by authors of information sources. Sentiment Analysis can be applied at different level of granularity and each of them still has open issues. In this paper we propose a completely unsupervised approach aimed at inducing a set of words patterns that change the polarity of subjective terms. This is a very important task because, while sentiment lexicons are valid tools that can be used to identify the polarity at word level, working at different level of granularity they are no longer sufficient, because of the various aspects to consider like the context, the use of negations and so on that can change the polarity of subjective terms.},

keywords = {Opinion Mining, Sentiment Analysis},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{terranaAutomaticUnsupervisedPolarity2014,

title = {Automatic Unsupervised Polarity Detection on a Twitter Data Stream},

author = { Diego Terrana and Agnese Augello and Giovanni Pilato},

doi = {10.1109/ICSC.2014.17},

isbn = {978-1-4799-4002-8},

year  = {2014},

date = {2014-01-01},

booktitle = {Proceedings - 2014 IEEE International Conference on Semantic Computing, ICSC 2014},

pages = {128--134},

publisher = {IEEE Computer Society},

abstract = {In this paper we propose a simple and completely automatic methodology for analyzing sentiment of users in Twitter. Firstly, we built a Twitter corpus by grouping tweets expressing positive and negative polarity through a completely automatic procedure by using only emoticons in tweets. Then, we have built a simple sentiment classifier where an actual stream of tweets from Twitter is processed and its content classified as positive, negative or neutral. The classification is made without the use of any pre-defined polarity lexicon. The lexicon is automatically inferred from the streaming of tweets. Experimental results show that our method reduces human intervention and, consequently, the cost of the whole classification process. We observe that our simple system captures polarity distinctions matching reasonably well the classification done by human judges. textcopyright 2014 IEEE.},

keywords = {Data Mining, Opinion Mining, Semantic Computing, Sentiment Analysis},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{terrana_automatic_2014,

title = {Automatic unsupervised polarity detection on a twitter data stream},

author = {Diego Terrana and Agnese Augello and Giovanni Pilato},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84906974590&doi=10.1109%2fICSC.2014.17&partnerID=40&md5=e52211941250a5a5d60b75df54e7f68c},

doi = {10.1109/ICSC.2014.17},

isbn = {978-1-4799-4002-8},

year  = {2014},

date = {2014-01-01},

booktitle = {Proceedings - 2014 IEEE International Conference on Semantic Computing, ICSC 2014},

pages = {128–134},

publisher = {IEEE Computer Society},

abstract = {In this paper we propose a simple and completely automatic methodology for analyzing sentiment of users in Twitter. Firstly, we built a Twitter corpus by grouping tweets expressing positive and negative polarity through a completely automatic procedure by using only emoticons in tweets. Then, we have built a simple sentiment classifier where an actual stream of tweets from Twitter is processed and its content classified as positive, negative or neutral. The classification is made without the use of any pre-defined polarity lexicon. The lexicon is automatically inferred from the streaming of tweets. Experimental results show that our method reduces human intervention and, consequently, the cost of the whole classification process. We observe that our simple system captures polarity distinctions matching reasonably well the classification done by human judges. © 2014 IEEE.},

keywords = {Data Mining, Opinion Mining, Semantic Computing, Sentiment Analysis},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{sangiorgi_approach_2014,

title = {An approach to detect polarity variation rules for sentiment analysis},

author = {Pierluca Sangiorgi and Agnese Augello and Giovanni Pilato},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84902376112&doi=10.5220%2f0004961903440349&partnerID=40&md5=55d13b87bbd12fa5b0b936f2e80da434},

doi = {10.5220/0004961903440349},

isbn = {978-989-758-024-6},

year  = {2014},

date = {2014-01-01},

booktitle = {WEBIST 2014 - Proceedings of the 10th International Conference on Web Information Systems and Technologies},

volume = {2},

pages = {344–349},

publisher = {SciTePress},

abstract = {Sentiment Analysis is a discipline that aims at identifying and extract the subjectivity expressed by authors of information sources. Sentiment Analysis can be applied at different level of granularity and each of them still has open issues. In this paper we propose a completely unsupervised approach aimed at inducing a set of words patterns that change the polarity of subjective terms. This is a very important task because, while sentiment lexicons are valid tools that can be used to identify the polarity at word level, working at different level of granularity they are no longer sufficient, because of the various aspects to consider like the context, the use of negations and so on that can change the polarity of subjective terms.},

keywords = {Opinion Mining, Sentiment Analysis},

pubstate = {published},

tppubtype = {inproceedings}

}