Seeing the wood for the trees: How machine learning can help firms in identifying relevant electronic word-of-mouth in social media

Vermeer, Susan A.M.; Araujo, Theo; Bernritter, Stefan F. and van Noort, Guda. 2019. Seeing the wood for the trees: How machine learning can help firms in identifying relevant electronic word-of-mouth in social media. International Journal of Research in Marketing, 36(3), pp. 492-508. ISSN 0167-8116 [Article]

[img] Text
Vermeer et al 2019 - IJRM.pdf - Accepted Version
Permissions: Administrator Access Only

Download (1MB)
[img] Text (HTM file)
Vermeer Araujo Bernritter van Noort Seeing the Wood for the Trees - ScienceDirect.htm - Additional Metadata
Permissions: Administrator Access Only

Download (323kB)
[img]
Preview
Text
Vermeer Araujo Bernritter van Noort 2019.pdf - Published Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (4MB) | Preview

Abstract or Description

The increasing volume of firm-related conversations on social media has made it considerably more difficult for marketers to track and analyse electronic word-of-mouth (eWOM) about brands, products or services. Firms often use sentiment analysis to identify relevant eWOM that requires a response to consequently engage in webcare. In this paper, we show that sentiment analysis of any kind might not be ideal for this purpose, because it relies on the questionable assumption that only negative eWOM is response-worthy and it is not able to infer meaning from text. We propose and test an approach based on supervised machine learning that first decides whether eWOM is relevant for the brand to respond, and then—based on a categorization of seven different types of eWOM (e.g., question, complaint)—classifies three customer satisfaction dimensions. Using a dataset of approximately 60,000 Facebook comments and 11,000 tweets about 16 different brands in eight different industries, we test and compare the efficacy of various sentiment analysis, dictionary-based and machine learning techniques to detect relevant eWOM. In doing so, this study identifies response-worthy eWOM based on the content instead of its expressed sentiment. The results indicate that these machine learning techniques achieve considerably higher accuracy in detecting relevant eWOM on social media compared to any kind of sentiment analysis. Moreover, it is shown that industry-specific classifiers can further improve this process and that algorithms are applicable across different social networks.

Item Type:

Article

Identification Number (DOI):

https://doi.org/10.1016/j.ijresmar.2019.01.010

Additional Information:

Confirmed CC BY-NC-ND; added VoR FF 29/4/2020; Published with Gold open access at landing page (https://doi.org/10.1016/j.ijresmar.2019.01.010, see HTM file). License missing from published PDF - requested for Elsevier to add FF 22/1/2020

Keywords:

eWOM, webcare, social media, digital marketing strategies, automated content analysis, sentiment analysis, machine learning

Departments, Centres and Research Units:

Institute of Management Studies

Dates:

DateEvent
28 January 2019Accepted
11 February 2019Published Online
1 September 2019Published

Item ID:

25697

Date Deposited:

30 Jan 2019 11:25

Last Modified:

29 Apr 2020 17:24

Peer Reviewed:

Yes, this version has been peer-reviewed.

URI:

http://research.gold.ac.uk/id/eprint/25697

View statistics for this item...

Edit Record Edit Record (login required)