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Sameerchand Pudaruth
University of Mauritius
Sharmila Moheeputh
University of Mauritius
Narmeen Permessur
University of Mauritius
Adeelah Chamroo
University of Mauritius
Vol. 7 No. 1 (2018), Articles, pages 41-48
Accepted: Feb 23, 2018


The number and size of social networks have grown significantly as years have passed. With its 1.5 billion active users, Facebook is by far the most popular social networks on the planet. From kindergarten kids to grandparents to teenagers, Facebook attracts users of all ages, religions, personalities and social status. Facebook users are sharing their personal information, their lifestyle, their precious moments and their feelings online. In this paper, we download a set of comments from the page ‘Opposing Views’ from Facebook. These were then categorised into either a positive comment or a negative comment using the auto code feature in NVivo 11. Comments where no positive or negative sentiments are found are considered to be neutral. Out of 626 comments, 29.6% were found to contain positive sentiments while 62.0% were found to contain negative sentiments. The outcome of this work can be used by businesses to assess public reviews about their products. This will help them understand what is working and what is not. Thus, they can improve their products and respond to customer demands sufficiently quickly.


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Anwar Hridoy, S. A., Ekram, M. T., Islam, M. S., Ahmed, F. and Rahman, R. M., (2015). Localized Twitter Opinion Mining using Sentiment Analysis. Decision Analytics, 2(8). doi:10.1186/s40165-015-0016-4. -

Bae, Y. and Lee, H., (2012). Sentiment Analysis of Twitter Audiences: Measuring the Positive or Negative influence of Popular Twitterers. Journal of the American Society for Information Science and Technology, 63(12), pp. 2521-2535. -

Caton, S., Hall, M. and Weinhardt, C. (2015). How do Politicians use Facebook? An Applied Social Observatory. Big Data & Society, July-December 2015, pp. 1-18. -

Dubreil, E., Vernier, M., Monceaux, L. and Daille, B., (2008). Annotating Opinion – Evaluation of Blogs. Workshop on Sentiment Analysis: Emotion, Metaphor, Ontology and Terminology (EMOT 2008), pp. 124, Marrakech, Morocco.

Gopaldas, A. (2014). Marketplace Sentiments. Journal of Consumer Research, 41(4), pp. 995-1014. -

Hilal, A. H. and Alabri, S. S. (2013). Using Nvivo for Data Analysis in Qualitative Research. International Interdisciplinary Journal of Education, 2(2), pp. 181-186. -

Iosub, D., Laniado, D., Castillo, C., Morell, M. F. and Kaltenbrunner, A., (2014). Emotions under Discussion: Gender, Status and Communication in Online Collaboration. PLoS ONE 9(8): e104880. doi:10.1371/journal.pone.0104880. -

Marrese-Taylor, E., Velasquez, J. D. and Bravo-Marquez, F., (2014). A Novel Deterministic Approach for Aspect-Based Opinion Mining in Tourism Product Review. Expert Systems with Applications, 41(17), pp. 7764-7775. -

Mihaltz, M., Varadi, T., Cserto, I., Fulop, E., Polya, T. and Kovago, P., (2015). Beyond Sentiment: Social Psychological Analysis of Political Facebook Comments in Hungary. In: Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA 2015), pp. 127-133, Lisbon, Portugal. -

OpposingViews, 2016. Opposing Views [online]. Available from: [Accessed 15 January 2016].

Pearce, W., Holmberg, K., Hellsten, I. and Nerlich, B., (2014). Climate Change on Twitter: Topics, Communities and Conversations about the 2013 IPCC Working Group 1 Report. PLoS ONE 9(4): e94785. doi:10.1371/journal.pone.0094785. -

Purao, S., Desouza, K. C. and Becker, J., (2012). Investigating Failures in Large-Scale Public Sector Projects with Sentiment Analysis. e-Service Journal, 8(2), pp. 84-105. -

Ravichandran, M., Kulanthaivel, G. and Chellatamilan, T., (2015). Intelligent Topical Sentiment Analysis for the Classification of E-Learners and their Topics of Interest. The Scientific World Journal. -

Sharef, N. (2014). A review of Sentiment Analysis Approaches in Big Data Era. In: Proceedings of the Malaysian National Conference on Databases, MaNCoD 2014, pp. 7-12, Serdang, Malaysia.

Sonnier, G., McAlister, L. and Rutz, O. J., (2011). A Dynamic Model of the Effect of Online Communications on Firm Sales. Marketing Science, 30(4), pp.702-716. -

Statista, 2016. The Statistics Portal [online]. Available from: [Accessed 15 January 2016].

Terrana, D., Augello, A., and Pilato, G., (2014). Facebook User Relationships Analysis based on Sentiment Classification. In: Proceedings of the 2014 IEEE International Conference on Semantic Computing. -

Thelwall, M., Wilkinson D. and Uppal, S. (2010). Data Mining Emotion in Social Network Communication: Gender Differences in MySpace. Journal of the American Society for Information Science & Technology, 61(1), pp. 190-199. -

TripAdvisor, 2016. TripAdvisor [online]. Available from: [Accessed 15 January 2016].

WordNet, 2016. WordNet: A Lexical Database for English [online]. Available from: [Accessed 15 January 2016].