Chapter Decomposing tourists’ sentiment from raw NL text to assess customer satisfaction
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 ef...
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| Fformat: | Online |
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Firenze University Press
2022
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| Mynediad Ar-lein: | ONIX_20220601_9788855183048_509 |
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Dim Tagiau, Byddwch y cyntaf i dagio'r cofnod hwn!
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| _version_ | 1869518811481243648 |
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| author | Romano, Maurizio MOLA, FRANCESCO CONVERSANO, CLAUDIO |
| author_browse | CONVERSANO, CLAUDIO MOLA, FRANCESCO Romano, Maurizio |
| author_facet | Romano, Maurizio MOLA, FRANCESCO CONVERSANO, CLAUDIO |
| author_sort | Romano, Maurizio |
| collection | Directory of Open Access Books |
| description | 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. |
| format | Online |
| id | doab-20.500.12854ir-82078 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Firenze University Press |
| publisherStr | Firenze University Press |
| record_format | ojs |
| spelling | doab-20.500.12854ir-820782022-06-02T04:03:29Z Chapter Decomposing tourists’ sentiment from raw NL text to assess customer satisfaction Romano, Maurizio MOLA, FRANCESCO CONVERSANO, CLAUDIO GSD WoM Threshold-based Naïve Bayes NLP Sentiment Analysis Customer Satisfaction 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. 2022-06-02T04:03:29Z 2022-06-02T04:03:29Z 2022-06-01T12:19:16Z 2021 chapter ONIX_20220601_9788855183048_509 2704-5846 https://library.oapen.org/handle/20.500.12657/56324 9788855183048 https://directory.doabooks.org/handle/20.500.12854/82078 eng Proceedings e report open access image/jpeg Attribution 4.0 International https://library.oapen.org/bitstream/20.500.12657/56324/1/16997.pdf Firenze University Press 10.36253/978-88-5518-304-8.29 10.36253/978-88-5518-304-8.29 2ec4474d-93b1-4cfa-b313-9c6019b51b1a 9788855183048 5 Florence open access |
| spellingShingle | GSD WoM Threshold-based Naïve Bayes NLP Sentiment Analysis Customer Satisfaction Romano, Maurizio MOLA, FRANCESCO CONVERSANO, CLAUDIO Chapter Decomposing tourists’ sentiment from raw NL text to assess customer satisfaction |
| title | Chapter Decomposing tourists’ sentiment from raw NL text to assess customer satisfaction |
| title_full | Chapter Decomposing tourists’ sentiment from raw NL text to assess customer satisfaction |
| title_fullStr | Chapter Decomposing tourists’ sentiment from raw NL text to assess customer satisfaction |
| title_full_unstemmed | Chapter Decomposing tourists’ sentiment from raw NL text to assess customer satisfaction |
| title_short | Chapter Decomposing tourists’ sentiment from raw NL text to assess customer satisfaction |
| title_sort | chapter decomposing tourists sentiment from raw nl text to assess customer satisfaction |
| topic | GSD WoM Threshold-based Naïve Bayes NLP Sentiment Analysis Customer Satisfaction |
| topic_facet | GSD WoM Threshold-based Naïve Bayes NLP Sentiment Analysis Customer Satisfaction |
| url | ONIX_20220601_9788855183048_509 |
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