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Mapping consumer sentiment toward wireless services using geospatial twitter data
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Qi, W., Procter, Rob, Zhang, J. and Guo, W. (2019) Mapping consumer sentiment toward wireless services using geospatial twitter data. IEEE Access, 7 . pp. 113726-113739. doi:10.1109/ACCESS.2019.2935200 ISSN 2169-3536.
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Official URL: http://doi.org/10.1109/ACCESS.2019.2935200
Abstract
Hyper-dense wireless network deployment is one of the popular solutions to meeting high capacity requirement for 5G delivery. However, current operator understanding of consumer satisfaction
comes from call centers and base station quality-of-service (QoS) reports with poor geographic accuracy. The dramatic increase in geo-tagged social media posts adds a new potential to understand consumer satisfaction towards target-specific quality-of-experience (QoE) topics. In our paper, we focus on evaluating users’ opinion on wireless service-related topics by applying natural language processing (NLP) to geo-tagged Twitter data. Current generalized sentiment detection methods with generalized NLP corpora are not topic specific. Here, we develop a novel wireless service topic-specific sentiment framework, yielding higher targeting accuracy than generalized NLP frameworks. To do so, we first annotate a new sentiment corpus called SignalSentiWord (SSW) and compare its performance with two other popular corpus libraries, AFINN and SentiWordNet. We then apply three established machine learning methods, namely: Naïve Bayes (NB), Support Vector Machine (SVM), and Recurrent Neural Network (RNN) to build our topic-specific sentiment classifier. Furthermore, we discuss the capability of SSW to filter noisy and high-frequency irrelevant words to improve the performance of machine learning algorithms. Finally, the real-world testing results show that our proposed SSW improves the performance of NLP significantly.
| Item Type: | Journal Article | |||||||||
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| Subjects: | H Social Sciences > HF Commerce H Social Sciences > HM Sociology Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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| Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | |||||||||
| Library of Congress Subject Headings (LCSH): | Wireless communication systems , 5G mobile communication systems , Quality of service (Computer networks) , Natural language processing (Computer science) , Social media , Social media -- Influence, Consumer behavior , Online social networks | |||||||||
| Journal or Publication Title: | IEEE Access | |||||||||
| Publisher: | IEEE | |||||||||
| ISSN: | 2169-3536 | |||||||||
| Official Date: | 13 August 2019 | |||||||||
| Dates: |
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| Volume: | 7 | |||||||||
| Page Range: | pp. 113726-113739 | |||||||||
| DOI: | 10.1109/ACCESS.2019.2935200 | |||||||||
| Status: | Peer Reviewed | |||||||||
| Publication Status: | Published | |||||||||
| Access rights to Published version: | Open Access (Creative Commons) | |||||||||
| Date of first compliant deposit: | 27 August 2020 | |||||||||
| Date of first compliant Open Access: | 28 August 2020 | |||||||||
| RIOXX Funder/Project Grant: |
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