By Mike Thelwall, Kevan Buckley, George Paltoglou, Marcin Skowron, David Garcia (auth.), Alexander Gelbukh (eds.)
This two-volume set, inclusive of LNCS 7816 and LNCS 7817, constitutes the completely refereed complaints of the thirteenth foreign convention on machine Linguistics and clever Processing, CICLING 2013, hung on Samos, Greece, in March 2013. the whole of ninety one contributions offered was once rigorously reviewed and chosen for inclusion within the court cases. The papers are geared up in topical sections named: common strategies; lexical assets; morphology and tokenization; syntax and named entity popularity; be aware experience disambiguation and coreference solution; semantics and discourse; sentiment, polarity, subjectivity, and opinion; computing device translation and multilingualism; textual content mining, details extraction, and knowledge retrieval; textual content summarization; stylometry and textual content simplification; and applications.
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Additional info for Computational Linguistics and Intelligent Text Processing: 14th International Conference, CICLing 2013, Samos, Greece, March 24-30, 2013, Proceedings, Part II
61) because annotators often fail to decide whether a segment is purely objective and thus if it conveys only facts or if a segment holds an implicit opinion. 66). Nonetheless, these ﬁgures are well in the range of state-of-the-art research reports in distinguishing between explicit and implicit opinions (see ). 55 for each class) and fair for the SI and O classes (resp. 34). 49). This shows that the newspaper reactions corpus was a bit more diﬃcult to annotate because the main topic is more diﬃcult to determine (even by the annotators) – it can be one of the subjects of the article, the article itself, its author(s), a previous comment or even a Measuring the Eﬀect of Discourse Structure on Sentiment Analysis 29 diﬀerent topic, related to various degrees to the subject of the article.
4 Results and Discussions Different feature vector generated after pre-processing are further used for the classification. Among different machine learning algorithms Support Vector Machine (SVM) classifiers are the mostly used for sentiment classification , , , , , . In our Experiments, BMNB and SVM are used for classifying review documents into positive or negative sentiment polarity, since BMNB can perform better than SVM in case some appropriate feature selection method is used.
72 for M R) whereas the kappa on the other points is fair. We get the same observation when computing agreements on segment’s strength. Results at the Discourse Level. Our goal here is to show the importance of discourse for opinion analysis and not to build a discourse bank that examine how well SDRT predicts the intuition of subjects, regardless of their knowledge of discourse theories. Therefore, computing inter-annotator agreements is out of the scope of this paper (for a detailed description of non-expert annotations using SDRT, see ).