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Unraveling E-WOM Patterns Using Text Mining and Sentiment Analysis

Unraveling E-WOM Patterns Using Text Mining and Sentiment Analysis
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Author(s): João Guerreiro (Instituto Universitario de Lisboa, Portugal)and Sandra Maria Correia Loureiro (Instituto Universitário de Lisboa, Portugal)
Copyright: 2020
Pages: 13
Source title: Exploring the Power of Electronic Word-of-Mouth in the Services Industry
Source Author(s)/Editor(s): Sandra Maria Correia Loureiro (Instituto Universitário de Lisboa, Portugal)and Hans Ruediger Kaufmann (University of Applied Management Studies, Germany)
DOI: 10.4018/978-1-5225-8575-6.ch006

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Abstract

Electronic word-of-mouth (e-WOM) is a very important way for firms to measure the pulse of its online reputation. Today, consumers use e-WOM as a way to interact with companies and share not only their satisfaction with the experience, but also their discontent. E-WOM is even a good way for companies to co-create better experiences that meet consumer needs. However, not many companies are using such unstructured information as a valuable resource to help in decision making: first, because e-WOM is mainly textual information that needs special data treatment and second, because it is spread in many different platforms and occurs in near-real-time, which makes it hard to handle. The current chapter revises the main methodologies used successfully to unravel hidden patterns in e-WOM in order to help decision makers to use such information to better align their companies with the consumer's needs.

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