- ASA 2021 Statistics and Information Systems for Policy Evaluation
- A cura di 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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Informazioni sul capitolo
Titolo del capitolo
Decomposing tourists’ sentiment from raw NL text to assess customer satisfaction
Autori
Maurizio Romano, Francesco Mola, Claudio Conversano
Lingua
English
DOI
10.36253/978-88-5518-304-8.29
Opera sottoposta a peer review
Anno di pubblicazione
2021
Copyright
© 2021 Author(s)
Licenza d'uso
Licenza dei metadati
Informazioni bibliografiche
Titolo del libro
ASA 2021 Statistics and Information Systems for Policy Evaluation
Sottotitolo del libro
Book of short papers of the opening conference
Curatori
Bruno Bertaccini, Luigi Fabbris, Alessandra Petrucci
Opera sottoposta a peer review
Anno di pubblicazione
2021
Copyright
© 2021 Author(s)
Licenza d'uso
Licenza dei metadati
Editore
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
Collana
Proceedings e report
ISSN della collana
2704-601X
e-ISSN della collana
2704-5846