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sentiment interaction is to cultivate learners’ sense of belonging to the community, so that learners are willing to stay in the community for a long time and maintain learning motivation at a high level ( Cho, Kim, & Choi, 2017 ). Academic sentiments are generally hidden in the text records of learning community activities, such as documents, statements, and sentences. Through the techniques of sentiment analysis, weight calculation, and semantic understanding, the sentiment experience related to learning processes can be observed. In this regard, academic sentiment

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. [2] Thomson Reuters, “Thomson Reuters Adds Unique Twitter and News Sentiment Analysis to Thomson Reuters Eikon” [Online]. Available: . [Accessed: Mar.8, 2018]. [3] L. Chen, G. Chen, and F. Wang

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

directions. 2 Related Work 2.1 Disease Outbreaks on Social Media Table 2 records some exemplary research on the disease outbreak on social media. It can be observed that social media data contribute to several types of research on disease outbreaks, including public opinion mining, sentiment analysis, semantic network analysis, disease outbreak detection, and so on; the methods applied to social media data analysis vary from manual coding to machine learning techniques; Twitter is a worldwide platform for collection and analysis of social media data, while for social

, enterprises and shopping platform. It has become a hot research topic in marketing and the popular research direction in the field of text mining [ 4 ]. Based on the sentiment analysis technology, this paper directly explores the consumers’ sentimental orientation to the attributes of commodity or service from comments text, analyzes the user satisfaction and preference, and then discusses how the consumers, commodity producers and online shopping platforms to manage and utilize online reviews effectively. 2 Literature Review Sentiment analysis, also known as opinion mining

. 1631-1642. [3] High, R. (2012), The Era of Cognitive Systems: An Inside Look at IBM Watson and How it Works , Redbooks and IBM Corporation, vol. 1. [4] Ferrucci, D. (2012), Introduction to “This is Watson” , IBM Journal of Research and Development, vol. 56, pp. 1-15. [5] Kushwanth, R., Sachin, A., Shambhavi, B. and Shobha, G. (2014), Sentiment Analysis of Twitter Data , International Journal of Advanced Research in Computer Engineering and Technology (IJARCET 2014), vol. 3, no. 12, pp. 4337-4342. [6] Pak, A. and Paroubek, P. (2010), Twitter as a Corpus for

assessed the effectiveness of the model against two real Twitter datasets. Sentiment analysis for Twitter textual data is also a hot topic. Two main methods have been utilized to classify the sentiment polarity: supervised machine learning method and rule-based method ( Hutto & Gilbert, 2014 ; Kim & Hovy, 2014 ). Tang et al. (2014) developed a new method that learned sentiment from specific word embedding and outperformed the previous top-performing system. Ren, Wang, and Ji (2016) used SVM for sentiment classification, and LDA was adopted for the improvement of

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 mining in Hungarian

, 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 2016, pp. 1–6, 2016. [9] S. Baccianella, A. Esuli, and

., 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 & V. Dimitrova (Eds.), Proceedings of 5 th European Conference on Technology Enhanced Learning