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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Palmero Aprosio, A., Corcoglioniti, F., Dragoni, M., Rospocher, M.: Supervised opinion frames detection with RAID. In: Gandon, F., et al. (eds.) SemWebEval 2015. CCIS, vol. 548, pp. 251–263. Springer, Heidelberg (2015). doi:10.1007/978-3-319-25518-7_22
Araújo, M., Gonçalves, P., Cha, M., Benevenuto, F.: iFeel: a system that compares and combines sentiment analysis methods. In: Proceedings of the 23rd International Conference on World Wide Web, WWW 2014 Companion, pp. 75–78. ACM, New York (2014). http://doi.acm.org/10.1145/2567948.2577013
Araújo, M., dos Reis, J.C., Pereira, A.M., Benevenuto, F.: An evaluation of machine translation for multilingual sentence-level sentiment analysis. In: Proceedings of the 31st Annual ACM Symposium on Applied Computing, Pisa, Italy, 4–8 April 2016, pp. 1140–1145 (2016). http://doi.acm.org/10.1145/2851613.2851817
Chung, J.K.-C., Wu, C.-E., Tsai, R.T.-H.: Polarity detection of online reviews using sentiment concepts: NCU IISR Team at ESWC-14 challenge on concept-level sentiment analysis. In: Presutti, V., et al. (eds.) SemWebEval 2014. CCIS, vol. 475, pp. 53–58. Springer, Heidelberg (2014)
Di Rosa, E., Durante, A.: App2check: a machine learning-based system for sentiment analysis of app reviews in Italian language. In: Proceedings of the International Workshop on Social Media World Sensors (Sideways)- Held in conjunction with LREC 2016, pp. 8–11 (2016). http://www.lrec-conf.org/proceedings/lrec2016/workshops/LREC2016Workshop-Sideways_Proceedings.pdf
Dragoni, M., Tettamanzi, A., da Costa Pereira, C.: Dranziera: an evaluation protocol for multi-domain opinion mining. In: Chair, N.C.C., Choukri, K., Declerck, T., Goggi, S., Grobelnik, M., Maegaard, B., Mariani, J., Mazo, H., Moreno, A., Odijk, J., Piperidis, S. (eds.) Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016), European Language Resources Association (ELRA), Paris, France, May 2016
Dragoni, M., Tettamanzi, A.G.B., da Costa Pereira, C.: A fuzzy system for concept-level sentiment analysis. In: Presutti, V., et al. (eds.) SemWebEval 2014. CCIS, vol. 475, pp. 21–27. Springer, Heidelberg (2014)
Hu, M., Liu, B.: Mining and summarizing customer reviews. In: KDD, pp. 168–177. ACM (2004)
Miller, G.A.: Wordnet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995). http://doi.acm.org/10.1145/219717.219748
Nakov, P., Ritter, A., Sara, R., Sebastiani, F., Stoyanov, V.: Semeval-2016 task 4: sentiment analysis in Twitter. In: Proceedings of the 10th International Workshop on Semantic Evaluation, Association for Computational Linguistics (2016). http://alt.qcri.org/semeval2016/task4/
Schouten, K., Frasincar, F.: The benefit of concept-based features for sentiment analysis. In: Gandon, F., et al. (eds.) SemWebEval 2015. CCIS, vol. 548, pp. 223–233. Springer, Heidelberg (2015). doi:10.1007/978-3-319-25518-7_19
Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C.D., Ng, A.Y., Potts, C.: Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 1631–1642. Association for Computational Linguistics, Stroudsburg, October 2013
Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., Kappas, A.: Sentiment strength detection in short informal text. JASIST 61(12), 2544–2558 (2010). http://dx.doi.org/10.1002/asi.21416
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-46565-4_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-46564-7
Online ISBN: 978-3-319-46565-4
eBook Packages: Computer ScienceComputer Science (R0)