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Rinalds Vīksna and Gints Jēkabsons

R eferences [1] K. Ravi and V. Ravi, “A survey on opinion mining and sentiment analysis: Tasks, approaches and applications,” Knowledge-Based Systems , vol. 89, pp. 14–46, 2015. https://doi.org/10.1016/j.knosys.2015.06.015 [2] Thomson Reuters, “Thomson Reuters Adds Unique Twitter and News Sentiment Analysis to Thomson Reuters Eikon” [Online]. Available: https://www.thomsonreuters.com/en/press-releases/2014/thomson-reuters-adds-unique-twitter-and-news-sentiment-analysisto-thomson-reuters-eikon.html . [Accessed: Mar.8, 2018]. [3] L. Chen, G

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Chuanming Yu, Xingyu Zhu, Bolin Feng, Lin Cai and Lu An

help potential visitors better understand tourist attractions so that they can choose their favorite scenic spots and avoid and reduce trouble throughout the tour. For the reasons mentioned above, there is a need to study Japanese reviews of Chinese attractions to improve the service of tourist attractions. In the scientific communities worldwide, a growing number of studies have focused on sentiment analysis of online reviews. There is a great need for new tools and algorithms which can automatically, efficiently and robustly process the large amounts of user

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Vasileios Kagklis, Anthi Karatrantou, Maria Tantoula, Chris T. Panagiotakopoulos and Vassilios S. Verykios

. Retrieved from http://sentic.net/sentic-computing.pdf 11. Cambria, E., Schuller, B., Xia, Y., & Havasi, C. (2013). New Avenues in Opinion Mining and Sentiment Analysis. IEEE Intelligent Systems, 28 (2), 15–21. doi:10.1109/MIS.2013.30. 12. Carlos, A.R. (2011). Social Network Analysis in Telecommunications. John Wiley & Sons. ISBN 978-1-118-01094-5. 13. Carsten, U., Boreau, K., & Stepanyan, K. (2010). Who students interact with? A social network analysis perspective on the use of Twitter in Language Learning. In M. Wolpers, P. Kirschner, M. Scheffel

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Katalin Tünde Jánosi-Rancz, Zoltán Kátai and Roland Bogosi

References [1] S. Baccianella, A. Esuli, F. Sebastiani, SentiWordNet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining, LREC 2010, Seventh International Conference on Language Resources and Evaluation , Valetta, Malta, May 17–23, 2010, pp. 2200–2204. ⇒188, 189, 198 [2] V. Basile, M. Nissim, Sentiment analysis on Italian tweets, Proc. 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis , June 14, 2013, Atlanta, Georgia, USA, pp. 100–107. ⇒198 [3] G. Berend, R. Farkas, Opinion

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Giuseppe D’Aniello, Matteo Gaeta, Francesca Loia, Marek Reformat and Daniele Toti

International Conference on Urban Planning, Regional Development and Information Society. CORP–Competence Center of Urban and Regional Planning, pp. 905–912, 2015. [7] A. Vakali, D. Chatzakou, V. A. Koutsonikola, and G. Andreadis, Social data sentiment analysis in smart environments-extending dual polarities for crowd pulse capturing. in DATA, pp. 175–182, 2013. [8] D. Toti and M. Rinelli, On the road to speed-reading and fast learning with CONCEPTUM, in Proceedings - 2016 International Conference on Intelligent Networking and Collaborative Systems, IEEE INCoS

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Mateusz Lango

References [1] Abbasi, A., France, S., Zhang, Z., Chen, H.: Selecting Attributes for Sentiment Classification Using Feature Relation Networks. IEEE Transactions on Knowledge and Data Engineering, 23 (3), 447-462 (2011). [2] Baccianella, S., Esuli, A., Sebastiani, F.: Sentiwordnet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. In Proc. of the Int. Conference on Language Resources and Evaluation (2010). [3] Blagus, R., Lusa, L.: SMOTE for high-dimensional class-imbalanced data. BMC Bioinformatics, 14 (1), 1471

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Samira Bordbar and Pirooz Shamsinejad

References 1. Ravi, K., V. Ravi. A Survey on Opinion Mining and Sentiment Analysis: Tasks, Approaches and Applications. – Knowledge-Based Systems, Vol. 89 , 2015, pp. 14-46. 2. Balazs, J. A., J. D. Velásquez. Opinion Mining and Information Fusion: A Survey. – Information Fusion, Vol. 27 , 2016, pp. 95-110. 3. Basari, A. S. H., B. Hussin, I. G. P. Ananta, J. Zeniarja. Opinion Mining of Movie Review Using Hybrid Method of Support Vector Machine and Particle Swarm Optimization. – Procedia Engineering, Vol. 53 , 2013, pp. 453-462. 4. Ye, Q

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Vanessa Torres van Grinsven and Ger Snijkers

.1177/0957926508088962. Blumer, H. 1973. Symbolic Interactionism: Perspectives and method. Prentice-Hall, Englewood Cliffs: New Jersey. Braun, V. and V. Clarke. 2006. “Using Thematic Analysis in Psychology.” Qualitative Research in Psychology 3: 77-101. Doi: http://dx.doi.org/10.1191/1478088706qp063oa. Coosto. 2014. The Facts Webpage. Available at: http://www.coosto.nl/home/about/feiten and in English at http://www.coosto.co.uk/home/about/facts (accessed March 2014). Daas, P.J.H. and M.J. Puts. 2014. “Social Media Sentiment and Consumer

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Franky, Ondřej Bojar and Kateřina Veselovská

References Baccianella, S., A. Esuli, and F. Sebastiani. Sentiwordnet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. In Proceedings of the Seventh conference on International Language Resources and Evaluation (LREC’10), Valletta, Malta, May. European Language Resources Association (ELRA), 2010. Bakliwal, Akshat, Piyush Arora, and Vasudeva Varma. Hindi subjective lexicon: A lexical resource for hindi adjective polarity classification. In Chair), Nicoletta Calzolari (Conference, Khalid Choukri

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Anthony Nosshi, Aziza Asem and Mohamed Badr Senousy

-459. 7. Hariadi, I., D. Nurjanah. Hybrid Attribute and Personality Based Recommender System for Book Recommendation. – In: Proc. of International Conference on Data and Software Engineering (ICoDSE’17), 2017, pp. 1-5. 8. Yang, S.-B., S.-H. Shin, Y. Joun, C. Koo. Exploring the Comparative Importance of Online Hotel Reviews’ Heuristic Attributes in Review Helpfulness: A Conjoint Analysis Approach. – J. Travel Tour. Mark., Vol. 34 , September 2017, No 7, pp. 963-985. 9. Wang, H., K. Guo. The Impact of Online Reviews on Exhibitor Behaviour: Evidence from Movie