2024-03-29T10:23:37Zhttps://gredos.usal.es/oai/requestoai:gredos.usal.es:10366/1383912022-02-07T16:12:21Zcom_10366_138329com_10366_122682com_10366_4666com_10366_3823col_10366_138330
Pudaruth, Sameerchand
Moheeputh, Sharmila
Permessur, Narmeen
Chamroo, Adeelah
2018-09-21T08:36:05Z
2018-09-21T08:36:05Z
2018-02-23
ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 7 (2018)
2255-2863
http://hdl.handle.net/10366/138391
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.
application/pdf
eng
Ediciones Universidad de Salamanca (España)
Attribution-NonCommercial-NoDerivs 3.0 Unported
https://creativecommons.org/licenses/by-nc-nd/3.0/
info:eu-repo/semantics/openAccess
Computación
Informótica
Computing
Information Technology
Sentiment Analysis from Facebook Comments using Automatic Coding in NVivo 11
info:eu-repo/semantics/article