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.
MultiWordNet is included into the Open Multilingual Wordnet project (http://compling.hss.ntu.edu.sg/omw/).
- 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.
A sentence is a linguistic unit consisting of one or more words that are grammatically linked.
- 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.
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.
References
Liu, B.: Sentiment Analysis and Opinion Mining. Morgan & Claypool Publishers, San Rafael (2012)
Polanyi, L., Zaenen, A.: Contextual valence shifters. In: Croft, W.B., Shanahan, J., Qu, Y., Wiebe, J. (eds.) Computing Attitude and Affect in Text: Theory and Applications. The Information Retrieval Series, vol. 20, pp. 1–10. Springer, Netherlands (2006)
Hassan, A., Korashy, H., Medhat, W.: Sentiment analysis algorithms and applications - a survey. Ain Shams Eng. J. 5, 1093–1113 (2014)
Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M.: Lexicon-based methods for sentiment analysis. Comput. Linguist. 37, 267–307 (2011)
Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the ACM International Conference on Knowledge Discovery & Data Mining (SIGKDD), pp. 168–177 (2004)
Littman, P., Turney, M.: Unsupervised learning of semantic orientation from a hundred-billion-word corpus. Technical report, National Research Council Canada, Institute for Information Technology (2002)
Fellbaum, C.: WordNet: An Electronic Lexical Database. MIT Press, Cambridge (1998)
Dragut, E.C., Yu, C., Sistla, P., Meng, W.: Construction of a sentimental word dictionary. In: ACM International Conference on Information and Knowledge Management (CIKM 2010), pp. 1761–1764 (2010)
Navigli, R.: Word sense disambiguation: a survey. ACM Comput. Surv. 41, 10:1–10:69 (2009)
Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing (HLT/EMNLP 2005), pp. 347–354 (2005)
Esuli, A., Sebastiani, F., Baccianella, S.: Sentiwordnet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: Proceedings of the 7th Conference on International Language Resources and Evaluation (LREC 2010), pp. 2200–2204 (2010)
Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C.D., Ng, A., Potts, C.: Recursive deep models for semantic compositionality over a sentiment treebank. In: EMNLP, pp. 1631–1642 (2013)
Kalchbrenner, N., Grefenstette, E., Blunsom, P.: A convolutional neural network for modelling sentences. In: ACL, pp. 655–665 (2014)
Tang, D., Qin, B., Liu, T.: Learning semantic representations of users and products for document level sentiment classification. In: ACL (1), Jul 2015, pp. 1014–1023 (2015)
Moraes, R., Valiati, J., Neto, W.: Document-level sentiment classification: an empirical comparison between SVM and ANN. Expert Syst. Appl. 40(2), 621–33 (2013)
Dong, L., Wei, F., Tan, C., Tang, D., Zhou, M., Xu, K.: Adaptive recursive neural network for target-dependent twitter sentiment classification. In: ACL (2), 22 June 2014, pp. 49–54 (2014)
Wang, G., Sun, J., Ma, J., Xu, K., Gu, J.: Sentiment classification: the contribution of ensemble learning. Decis. Support Syst. 57, 77–93 (2014)
Ho, T.: The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 832–844 (1998)
Buschken, J., Allenby, G.M.: Sentence-based text analysis for customer reviews. Mark. Sci. 35(6), 953–75 (2016)
Ordenes, F.V., Ludwig, S., Ruyter, K.D., Grewal, D., Wetzels, M.: Unveiling what is written in the stars: analyzing explicit, implicit, and discourse patterns of sentiment in social media. J. Consum. Res. 43(6), 875–894 (2017)
Kim, S.M., Hovy, E.: Identifying and analyzing judgment opinions. In: Proceedings of the Joint Human Language Technology/North American Chapter of the ACL Conference (HLT-NAACL-06), pp. 200–207 (2006)
Kanayama, H., Nasukawa, T.: Fully automatic lexicon expansion for domain-oriented sentiment analysis. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2006), pp. 355–363 (2006)
Takamura, H., Inui, T., Okumura, M.: Latent variable models for semantic orientations of phrases. In: Proceedings of the 11th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2006), pp. 201–208 (2006)
Abbasi, A., Hsinchun, C., Arab, S.: Sentiment analysis in multiple languages: feature selection for opinion classification in web forums. ACM Trans. Inf. Syst. 26, 1–34 (2008)
Casoto, P., Dattolo, A., Tasso, C.: Sentiment classification for the italian language: a case study on movie reviews. J. Internet Technol. 9, 365–373 (2008)
Bautin, M., Vijayarenu, L., Skiena, S.: International sentiment analysis for news and blogs. In: Proceedings of the International Conference on Weblogs and Social Media (ICWSM 2008), pp. 19–26 (2008)
Bentivogli, L., Girardi, C., Pianta, E.: Multiwordnet, developing an aligned multilingual database. In: Proceedings of the First International Conference on Global WordNet, pp. 293–302 (2002)
Agerri, R., Garcia-Serrano, A.: Q-wordnet: extracting polarity from wordnet senses. In: Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC 2010) (2010)
Cambria, E., Olsher, D., Rajagopal, D.: Senticnet 3: a common and common-sense knowledge base for cognition-driven sentiment analysis. In: Twentyeight AAAI Conference on Artificial Intelligence (AAAI-14), pp. 1515–1521 (2014)
Strapparava, C., Valitutti, A.: Wordnet-affect: an affective extension of wordnet. In: Proceedings of the 4th International Conference on Language Resources and Evaluation (LREC 2004), pp. 1083–1086 (2004)
Compagnoni, S., Demontis, V., Formentelli, A., Gandini, M., Cerini, G.: Micro-WNOp: a gold standard for the evaluation of automatically compiled lexical resources for opinion mining. In: Language Resources and Linguistic Theory: Typology, Second Language Acquisition, English linguistics. Franco Angeli Editore (2007)
Zanchetta, E., Baroni, M.: Morph-it! a free corpus-based morphological resource for the Italian language. In: Proceedings of Corpus Linguistics 2005 (2005)
Koppel, M., Schler, J.: The importance of neutral examples for learning sentiment. Comput. Intell. 22(2), 100–109 (2006)
Hammer, H., Yazidi, A., Bai, A., Engelstad, P.: Building domain specific sentiment lexicons combining information from many sentiment lexicons and a domain specific corpus. In: Amine, A., Bellatreche, L., Elberrichi, Z., Neuhold, J.E., Wrembel, R. (eds.) Computer Science and Its Applications: 5th IFIP TC 5 International Conference, CIIA 2015. Saida, Algeria (2015)
Hamilton, W.L., Clark, K., Leskovec, J., Jurafsky, D.: Inducing domain-specific sentiment lexicons from unlabeled corpora (2016). http://nlp.stanford.edu/projects/socialsent
Agathangelou, P., Katakis, I., Kokkoras, F., Ntonas, K.: Mining domain-specific dictionaries of opinion words. In: 15th International Conference on Web Information System Engineering (WISE 2014), pp. 47–62 (2014)
Chiavetta, F., Lo Bosco, G., Giovanni, P.: A lexicon-based approach for sentiment classification of Amazon books reviews in Italian language. In: 12th International Conference on Web Information Systems and Technologies (WEBIST), pp. 159–170 (2016)
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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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