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Guerreiro, J. & Loureiro, S. M. C. (2020). Unraveling e-WOM patterns using text mining and sentiment analysis. In Sandra Maria Correia Loureiro,  Hans Ruediger Kaufmann (Ed.), Exploring the power of electronic word-of-mouth in the services industry. Hershey: IGI Global.

J. R. Guerreiro and S. M. Loureiro,  "Unraveling e-WOM patterns using text mining and sentiment analysis", in Exploring the power of electronic word-of-mouth in the services industry, Sandra Maria Correia Loureiro,  Hans Ruediger Kaufmann, Ed., Hershey, IGI Global, 2020

@incollection{guerreiro2020_1734886061278,
	author = "Guerreiro, J. and Loureiro, S. M. C.",
	title = "Unraveling e-WOM patterns using text mining and sentiment analysis",
	chapter = "",
	booktitle = "Exploring the power of electronic word-of-mouth in the services industry",
	year = "2020",
	volume = "",
	series = "",
	edition = "",
	publisher = "IGI Global",
	address = "Hershey",
	url = "https://www.igi-global.com/book/exploring-power-electronic-word-mouth/218512"
}

TY  - CHAP
TI  - Unraveling e-WOM patterns using text mining and sentiment analysis
T2  - Exploring the power of electronic word-of-mouth in the services industry
AU  - Guerreiro, J.
AU  - Loureiro, S. M. C.
PY  - 2020
DO  - 10.4018/978-1-5225-8575-6.ch006
CY  - Hershey
UR  - https://www.igi-global.com/book/exploring-power-electronic-word-mouth/218512
AB  - 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.
ER  -