Guo, Weisi and Zhang, Jie (2017) Uncovering wireless blackspots using Twitter data. Electronics Letters, 53 (12). pp. 814-816. ISSN 0013-5194.
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Abstract
Blackspots are areas of poor signal coverage or service delivery that leads to customer complaints and loss in business revenue. Understanding their spatial-temporal patterns at a high resolution is important for interventions. Conventional methods such as customer helplines, drive-by testing, and network analysis tools often lack the real-time capability and spatial accuracy required. In this paper, we investigate the potential of utilizing geo-tagged Twitter data to uncover blackspots. Here, we apply lexicon and machine-learning natural language processing techniques to over 1.4 million Tweets in London to uncover blackspots for both pre-4G (2012) and post-4G (2016) roll out. It was found that long-term poor signal complaints make up the majority of complaints (86%) pre-4G roll out, but short-term network failure was responsible for most complaints (66%) post-4G roll out.
| Item Type: | Journal Article |
|---|---|
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering |
| Library of Congress Subject Headings (LCSH): | Wireless communication systems -- Testing -- London (England), Twitter (Firm) |
| Journal or Publication Title: | Electronics Letters |
| Publisher: | Institution of Engineering and Technology |
| ISSN: | 0013-5194 |
| Official Date: | 8 June 2017 |
| Dates: | Date Event 8 June 2017 Published 4 April 2017 Accepted |
| Volume: | 53 |
| Number: | 12 |
| Page Range: | pp. 814-816 |
| Status: | Peer Reviewed |
| Publication Status: | Published |
| Access rights to Published version: | Restricted or Subscription Access |
| Date of first compliant deposit: | 2 May 2017 |
| Date of first compliant Open Access: | 11 January 2018 |
| URI: | https://wrap.warwick.ac.uk/88037/ |
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