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Triangulated sentiment analysis of tweets for social CRM
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Griesser, Simone E. and Gupta, Neha (2019) Triangulated sentiment analysis of tweets for social CRM. In: 2019 6th Swiss Conference on Data Science (SDS), Bern, Switzerland, 14 Jun 2019. Published in: 2019 6th Swiss Conference on Data Science (SDS) ISBN 9781728131054. doi:10.1109/SDS.2019.000-4
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Official URL: https://doi.org/10.1109/SDS.2019.000-4
Abstract
High resolution data from social media platforms like Twitter presents an unprecedented opportunity to organisations for social customer relationship management (Social CRM) by analysing the ongoing discussion about business events such as a service outage. Text based sentiment analysis has been widely researched utilising mainly lexicon-based and machine learning approaches to uncover customers' opinions. They are similar in the sense that the machine learning approach relies on an initial lexical model on which the learning is based. Both methods view sentiment as either positive, neutral, or negative. This is not the case for the psycholinguistic approach following which text sentiment is more continuous. We compare these three approaches with a Twitter dataset collected during a service outage. Contrary to our expectation, we find that the language used in tweets is not very negative or emotionally intense. This research therefore contributes to the sentiment analysis discussion by dissecting three methods and illustrating how and why they arrive at differing results. The selected research context provides an illuminating case about service failure and recovery.
| Item Type: | Conference Item (Paper) | ||||||
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| Subjects: | B Philosophy. Psychology. Religion > BF Psychology H Social Sciences > HF Commerce H Social Sciences > HM Sociology P Language and Literature > P Philology. Linguistics Z Bibliography. Library Science. Information Resources > ZA Information resources |
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| Divisions: | Faculty of Social Sciences > Warwick Business School | ||||||
| Library of Congress Subject Headings (LCSH): | Social media , User-generated content , Social media -- Economic aspects, Social media and society, Text data mining , Sentiment analysis , Psycholinguistics , Customer relations -- Management, Microblogs -- Psychological aspects, Microblogs -- Data processing | ||||||
| Journal or Publication Title: | 2019 6th Swiss Conference on Data Science (SDS) | ||||||
| Publisher: | IEEE | ||||||
| ISBN: | 9781728131054 | ||||||
| Official Date: | 8 August 2019 | ||||||
| Dates: |
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| DOI: | 10.1109/SDS.2019.000-4 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Reuse Statement (publisher, data, author rights): | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||
| Date of first compliant deposit: | 17 May 2021 | ||||||
| Date of first compliant Open Access: | 17 May 2021 | ||||||
| RIOXX Funder/Project Grant: |
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| Conference Paper Type: | Paper | ||||||
| Title of Event: | 2019 6th Swiss Conference on Data Science (SDS) | ||||||
| Type of Event: | Conference | ||||||
| Location of Event: | Bern, Switzerland | ||||||
| Date(s) of Event: | 14 Jun 2019 |
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