Please use this identifier to cite or link to this item:
Loureiro, S. M. C.
|Editor:||Sandra Maria Correia Loureiro|
Hans Ruediger Kaufmann
|Title:||Unraveling e-WOM patterns using text mining and sentiment analysis|
|Book title/volume:||Exploring the power of electronic word-of-mouth in the services industry|
|Reference:||Guerreiro, J., Loureiro, S. M. C. (2020). Unraveling e-WOM patterns using text mining and sentiment analysis. EM Sandra Maria Correia Loureiro, Hans Ruediger Kaufmann (Eds.). Exploring the power of electronic word-of-mouth in the services industry. IGI Global. 10.4018/978-1-5225-8575-6.ch006|
|DOI (Digital Object Identifier):||10.4018/978-1-5225-8575-6.ch006|
Análise de sentimentos -- Sentiment analysis
|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.|
|Access type:||Open Access|
|Appears in Collections:||BRU-CLI - Capítulos de livros internacionais|
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