Classifying Italian newspaper text: news or editorial?
Authors: Pietro Totis, Manfred Stede (Università di Udine, University of Potsdam, 2018)
Topic: Textual Genres & Literature Linguistics

Abstract
We present a text classiﬁer that English. We present a text classiﬁer that can distinguish Italian news stories from editorials. Inspired by earlier work on pus and implemented a range of features, which can predict the distinction with an accuracy of 89,12%. As demonstrated by the earlier work, such a feature-based approach outperforms simple bag-of-words models when being transferred to new domains. We argue that the technique can also be used to distinguish opinionated from non-opinionated text outside of the realm of newspapers.

Introduction
The computational task of text classiﬁcation is typically targeting the question of domain: Is a text about sports, the economy, local politics, etc. But texts can also be grouped by their genre: Is it a business letter, a personal homepage, a cooking recipe, and so on. In this paper, we perform genre classiﬁcation on newspaper text and are speciﬁcally interested in the question whether a text communicates a news report or gives an opinion, i.e., it is an editorial (or some similar opinionated piece). This task is relevant for many information extraction applications based on newspaper text, and it can also be extended from newspapers to other kinds of text, where the distinction ”opinionated or not” is of interest, as in sentiment analysis or argumentation mining. Our starting point is the work by (Kr¨uger et al., 2017), who presented a news/editorial classiﬁer for English. They demonstrated that using linguistically-motivated features leads to better results than bag-of-words or POS-based models, when it comes to changing the domain of text (which newspaper, which time of origin, which type of content). To transfer the approach to Italian, we assembled a suitable corpus for training and testing, selected preprocessing tools, and adapted the features used by the classiﬁer from Kr¨uger et al. Our results are in same range of the original work, indicating that the problem can be solved for Italian in pretty much the same way. We found some differences in the relative feature strengths, however. After considering related work in Section 2, we describe our corpus (Section 3) and the classiﬁcation experiments (Section 4), and then conclude.

Conclusion
We presented, to our knowledge, the ﬁrst classiﬁer that is able to distinguish ‘news’ from ‘editorials’ in an Italian newspaper corpus. It follows a linguistic feature-oriented approach proposed by (Kr¨uger et al., 2017) for English, who had demonstrated that it outperforms lexical and POS-based models. In our implementation, With an accuracy of 89.09% the distinction between the two subgenres can be drawn quite reliably. Our results are comparable to that of Kr¨uger et al., which indicates (again, to our knowledge for the ﬁrst time) that their feature space is applicable successfully to languages other than English. Our central concern for this kind of task is robustness against domain changes of different kinds. To this end, Kr¨uger et al. had worked with different newspaper sources and demonstrated the utility of the feature approach in such settings. While we were not able to assemble large corpora from different papers, we ran other experiments in the same vein, where the ﬁrst shows that the system is robust against changing the portions of the newspapers (i.e., economy versus local affairs, and so on). In the second one, we applied the classiﬁer, as trained on the newspaper data, to the distinction between Italian Wikipedia articles and Amazon reviews, where the results remained stable as well. We take this as an indication that the classiﬁer captures a general difference between ‘opinionated’ and ‘non-opinionated’ text, and not just some ‘ad hoc’ phenomena of certain newspaper sub-genres.