- ASA 2021 Statistics and Information Systems for Policy Evaluation
- Edited by Bruno Bertaccini, Luigi Fabbris, Alessandra Petrucci
Decomposing tourists’ sentiment from raw NL text to assess customer satisfaction
- Maurizio Romano
- Francesco Mola
- Claudio Conversano
- © 2021 Author(s) |
- CC BY 4.0
- DOI: 10.36253/978-88-5518-304-8.29
The importance of the Word of Mouth is growing day by day in many topics. This phenomenon is evident in everyday life, e.g., the rise of influencers and social media managers. If more people positively debate specific products, then even more people are encouraged to buy them and vice versa. This effect is directly affected by the relationship between the potential customer and the reviewer. Moreover, considering the negative reporting bias is evident in how the Word of Mouth analysis is of absolute interest in many fields. We propose an algorithm to extract the sentiment from a natural language text corpus. The combined approach of Neural Networks, with high predictive power but more challenging interpretation, with more simple but informative models, allows us to quantify a sentiment with a numeric value and to predict if a sentence has a positive (negative) sentiment. The assessment of an objective quantity improves the interpretation of the results in many fields. For example, it is possible to identify crucial specific sectors that require intervention, improving the company's services whilst finding the strengths of the company himself (useful for advertising campaigns). Moreover, considering that the time information is usually available in textual data with a web origin, to analyze trends on macro/micro topics. After showing how to properly reduce the dimensionality of the textual data with a data-cleaning phase, we show how to combine: WordEmbedding, K-Means clustering, SentiWordNet, and the Threshold-based Naïve Bayes classifier. We apply this method to Booking.com and TripAdvisor.com data, analyzing the sentiment of people who discuss a particular issue, providing an example of customer satisfaction.
- Keywords:
- GSD,
- WoM,
- Threshold-based Naïve,
- Bayes,
- NLP,
- Sentiment Analysis,
- Customer Satisfaction,
University of Cagliari, Italy - ORCID: 0000-0001-8947-2220
University of Cagliari, Italy - ORCID: 0000-0001-6076-1600
University of Cagliari, Italy - ORCID: 0000-0003-2020-5129
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Chapter Information
Chapter Title
Decomposing tourists’ sentiment from raw NL text to assess customer satisfaction
Authors
Maurizio Romano, Francesco Mola, Claudio Conversano
Language
English
DOI
10.36253/978-88-5518-304-8.29
Peer Reviewed
Publication Year
2021
Copyright Information
© 2021 Author(s)
Content License
Metadata License
Bibliographic Information
Book Title
ASA 2021 Statistics and Information Systems for Policy Evaluation
Book Subtitle
Book of short papers of the opening conference
Editors
Bruno Bertaccini, Luigi Fabbris, Alessandra Petrucci
Peer Reviewed
Publication Year
2021
Copyright Information
© 2021 Author(s)
Content License
Metadata License
Publisher Name
Firenze University Press
DOI
10.36253/978-88-5518-304-8
eISBN (pdf)
978-88-5518-304-8
eISBN (xml)
978-88-5518-305-5
Series Title
Proceedings e report
Series ISSN
2704-601X
Series E-ISSN
2704-5846