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A Layered Architecture for Sentiment Classification of Products Reviews in Italian Language

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Web Information Systems and Technologies (WEBIST 2016)

Abstract

The paper illustrates a system for the automatic classification of the sentiment orientation expressed into reviews written in Italian language. A proper stratification of linguistic resources is adopted in order to solve the lacking of an opinion lexicon specifically suited for the Italian language. Experiments show that the proposed system can be applied to a wide range of domains.

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Notes

  1. 1.

    MultiWordNet is included into the Open Multilingual Wordnet project (http://compling.hss.ntu.edu.sg/omw/).

  2. 2.

    In this model the Wordnet for a foreign language is built by adding synsets in correspondence with the PWN synsets, whenever possible, and importing semantic relations from PWN by assuming that, if there are two synsets in PWN and a relation holding between them, the same relation holds between the corresponding synsets in the foreign language.

  3. 3.

    A sentence is a linguistic unit consisting of one or more words that are grammatically linked.

  4. 4.

    We remember that in the used resources both positive and negative polarity scores are unsigned values in the range [0.0, 1.0].

  5. 5.

    During the development of the proposed methodology, we have found that some terms in SentiWordNet have opposite polarity signs with respect to the corresponding Italian terms. Moreover, some errors are due to the POS tagger which in some cases applies wrong tags labelling some adjectives as verbs and some verbs as nouns.

  6. 6.

    https://github.com/steelcode/sentiment-lang-italian.

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Chiavetta, F., Lo Bosco, G., Pilato, G. (2017). A Layered Architecture for Sentiment Classification of Products Reviews in Italian Language. In: Monfort, V., Krempels, KH., Majchrzak, T., Traverso, P. (eds) Web Information Systems and Technologies. WEBIST 2016. Lecture Notes in Business Information Processing, vol 292. Springer, Cham. https://doi.org/10.1007/978-3-319-66468-2_7

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  • DOI: https://doi.org/10.1007/978-3-319-66468-2_7

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