Abstract
App2Check is a web application and an engine for opinion mining applied to user comments evaluating apps published in app stores. It includes features ranging from topic extraction, sentiment analysis of user reviews and topics, sentiment vs rating chronological trend, sentiment trend comparison between competitors, and many others. App2Check goal is to help app owners and makers to evaluate in real time their own apps, compare them with the apps available in the market, and extract from this analysis useful insights to perform a continuous improvement during both design and maintenance process. In this paper we describe App2Check features, by focusing in particular on the ones applying semantic and sentiment analysis to apps reviews, and we present an experimental comparison respect to 19 research tools. Then we show App2Check performance when applied to Amazon products reviews. In this experimental evaluation, we show App2Check performance with and without a specific training on Amazon products reviews, and we compare our results with two state-of-the-art research tools.
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Di Rosa, E., Durante, A. (2016). App2Check Extension for Sentiment Analysis of Amazon Products Reviews. In: Sack, H., Dietze, S., Tordai, A., Lange, C. (eds) Semantic Web Challenges. SemWebEval 2016. Communications in Computer and Information Science, vol 641. Springer, Cham. https://doi.org/10.1007/978-3-319-46565-4_7
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DOI: https://doi.org/10.1007/978-3-319-46565-4_7
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