By Henrik Boström, Arno Knobbe, Carlos Soares, Panagiotis Papapetrou
This ebook constitutes the refereed convention complaints of the fifteenth overseas convention on clever facts research, which used to be held in October 2016 in Stockholm, Sweden.
The 36 revised complete papers offered have been conscientiously reviewed and chosen from seventy five submissions. the conventional concentration of the IDA symposium sequence is on end-to-end clever help for facts research. The symposium goals to supply a discussion board for uplifting learn contributions that may be thought of initial in different top meetings and journals, yet that experience a possibly dramatic influence.
Read or Download Advances in Intelligent Data Analysis XV: 15th International Symposium, IDA 2016, Stockholm, Sweden, October 13-15, 2016, Proceedings PDF
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Extra resources for Advances in Intelligent Data Analysis XV: 15th International Symposium, IDA 2016, Stockholm, Sweden, October 13-15, 2016, Proceedings
PRMs are interested in manipulating structured representation of the data, involving objects described by attributes and participating in relationships, actions, and events. The probability model speciﬁcation concerns classes of objects rather than simple attributes. In order to be used, PRMs have to be constructed either by an expert or using learning algorithms. PRM learning implies ﬁnding a graphical structure as well as a set of conditional probability distributions that ﬁt the best way to the relational training data.
So the combination of speciﬁc, related words as well as detailed descriptions of the foundations were used to code the Tweets. Manual labeling of Tweets is a challenging task, given the inherent ambiguity of some Tweets, the short length of Tweets, the potential of multiple moral foundations being covered in Tweets and use of writing styles such as irony, sarcasm, satire or mere trolling. We choose not to ﬁlter out hard to label Tweets as this could bias the sample, nor did we want to include an ‘unknown’ category, as it would limit the usefulness of the model for monitoring.
It is also the ﬁrst study to examine moral expressions of the public regarding the Grexit. It uses generally accepted machine learning techniques to explore moral expressions in a natural real world setting, through the use of the Twitter platform. Speciﬁcally, this study will determine if supervised machine learning models are able to classify Tweets into moral foundations at an acceptable accuracy, potentially without relying on handcrafted dictionaries. The remainder of this paper is structured as follows; Sect.