A Hungarian Sentiment Corpus Manually Annotated at Aspect Level

A Hungarian Sentiment Corpus Manually Annotated at Aspect Level Martina Katalin Szab´o1,2, Veronika Vincze3,4, Katalin Simk´o4, Viktor Varga4, Viktor Hangya4 1Precognox Ltd 2University of Szeged, Institute of Slavonic Studies, Department of Russian Philology 3MTA-SZTE Research Group on Artificial Intelligence 4University of Szeged, Department of Informatics mszabo@precognox.com, szabo.martina@lit.u-szeged.hu; vinczev@inf.u-szeged.hu; kata.simko@gmail.com; wyvick@gmail.com; hangyav@inf.u-szeged.hu Abstract In this paper we present a Hungarian sentiment corpus manually annotated at aspect level. Our corpus consists of Hungarian opinion texts written about different types of products. The main aim of creating the corpus was to produce an appropriate database providing possibilities for developing text mining software tools. The corpus is a unique Hungarian database: to the best of our knowledge, no digitized Hungarian sentiment corpus that is annotated on the level of fragments and targets has been made so far. In addition, many language elements of the corpus, relevant from the point of view of sentiment analysis, got distinct types of tags in the annotation. In this paper, on the one hand, we present the method of annotation, and we discuss the difficulties concerning text annotation process. On the other hand, we provide some quantitative and qualitative data on the corpus. We conclude with a description of the applicability of the corpus. Keywords: Hungarian language, sentiment analysis, text mining, corpus, manual annotation 1. Introduction In this paper we present a Hungarian sentiment corpus man- ually annotated at the aspect level. First, we describe the method of annotation, and discuss the difficulties concern- ing text annotation process. Later, we present some quanti- tative and qualitative data on the corpus. Opinions have essential influence on almost all human ac- tivities and behaviors (Liu, 2012). This is not only true for individuals but also true for organizations. For this rea- son, in recent years, sentiment analysis (also called opinion mining) is widely studied in data mining, web mining, and text mining. Since the early 2000s, sentiment analysis has grown to be one of the most active research areas in nat- ural language processing (Liu, 2012). At the same time, in contrast to the international research, sentiment analysis of texts written in Hungarian has so far attracted less at- tention (Berend and Farkas, 2008; Mih´altz, 2010; Hangya et al., 2015). As for Hungarian databases, to the best of our knowledge, only one Hungarian sentiment corpus has been made so far, the OpinHuBank corpus (Mih´altz, 2013). However, this corpus does not contain deep sentiment an- notation (Szab´o and Vincze, 2015), therefore it can be used for linguistic research and NLP purposes only in a limited way. Based on the above, we decided to create a Hungarian sen- timent corpus, annotated manually at the level of fragments and targets, as well as for different types of sentiment mod- ifiers, which we will present in this paper. 2. Literature review Sentiment analysis is the field of language technology that analyzes people’s opinions, evaluations, appraisals and at- titudes towards entities such as products, services, organi- zations, individuals, issues or events (Liu, 2012). There are also …

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