2017
DOI: 10.1016/j.asoc.2016.09.011
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An ANN-based approach of interpreting user-generated comments from social media

Abstract: The IT advancement facilitates growth of social media networks, which allow consumers to exchange information online. As a result, a vast amount of user-generated data is freely available via Internet. These data, in the raw format, are qualitative, unstructured and highly subjective thus they do not generate any direct value for the business. Given this potentially useful database it is beneficial to unlock knowledge it contains. This however is a challenge, which this study aims to address. This paper propos… Show more

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Cited by 19 publications
(8 citation statements)
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“…They are recognized as the key communication channels during crisis, such as Twitter, Facebook, Sina Weibo, and WeChat. They can provide a large amount of volunteered information, such as observation, opinion, feeling, and psychological demand, [1][2][3] which helps decision makers to take action more quickly through providing collective intelligence. 4,5 A growing number of studies have explored the use of social media by emergency managers 6 and government officials, 7 as well as the general public.…”
mentioning
confidence: 99%
“…They are recognized as the key communication channels during crisis, such as Twitter, Facebook, Sina Weibo, and WeChat. They can provide a large amount of volunteered information, such as observation, opinion, feeling, and psychological demand, [1][2][3] which helps decision makers to take action more quickly through providing collective intelligence. 4,5 A growing number of studies have explored the use of social media by emergency managers 6 and government officials, 7 as well as the general public.…”
mentioning
confidence: 99%
“…Several studies have been conducted to confirm the advantages of BP neural networks in prediction. For example, Wong and Chan (2015) [8] found that BP neural networks perform significantly better than linear regression and SVR.Lee, Choeh (2014) [9] and Wong et al (2017) [10] also found that the prediction performance of BP neural network models outperforms linear regression models.…”
Section: Bp Neural Networkmentioning
confidence: 99%
“…[41]. However, though Wong et al [42] used neural networks to model what constitutes a useful comment made in an online domain and predict whether or not any new comments yield helpful information, as of yet no other work has employed deep learning to serve as a recommender system in a discussion forum to the best of the author's knowledge.…”
Section: Deep Learningmentioning
confidence: 99%