Viktor Hangya

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In this paper we introduce our contribution to the RepLab 2013 – An evaluation campaign for Online Reputation Management Systems challenge. We participated in the filtering and polarity detection subtasks. The task of filtering is to determine whether a tweet is related to an entity. Then we classify tweets into positive, negative or neutral classes from(More)
In this paper we introduce our contribution to the SemEval-2013 Task 2 on “Sentiment Analysis in Twitter”. We participated in “task B”, where the objective was to build models which classify tweets into three classes (positive, negative or neutral) by their contents. To solve this problem we basically followed the supervised learning approach and proposed(More)
In this paper, we introduce our contributions to the SemEval-2014 Task 4 – Aspect Based Sentiment Analysis (Pontiki et al., 2014) challenge. We participated in the aspect term polarity subtask where the goal was to classify opinions related to a given aspect into positive, negative, neutral or conflict classes. To solve this problem, we employed supervised(More)
People express their opinions about things like products, celebrities and services using social media channels. The analysis of these textual contents for sentiments is a gold mine for marketing experts as well as for research in humanities, thus automatic sentiment analysis is a popular area of applied artificial intelligence. The chief objective of this(More)
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(More)
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