Improving the tourism marketing strategies by predicting the behavior of travelers using social media networks
| Art der Publikation: | Artikel in einem Konferenzbericht |
| Zitat: | |
| Zeitschrift: | SwissText 2017, 2nd Swiss Text Analystics Conference, Winterthur, Switzerland, June 9th, 2017 |
| Jahr: | 2017 |
| Monat: | Juni |
| Ort: | Winterthur, Switzerland |
| Organisation: | ZHAW |
| Abriss: | We present a method to analyze social media networks for improving the marketing strategies of resorts using machine learning prediction. The raw information is provided by more than 200’000 pictures posted by tourists on Instagram from specific region of Switzerland during 3 years. Based on captions and comments of travelers, local weather (temperature, snow and sun) and national holidays, changes in the users’ vocabulary and expressions are detected and matched to a specific period of the season. Then, for each period (winter for this use case), the semantic knowledge is extracted through our advanced text analyzer and used to predict clients’ social media behavior at a given week of the winter. This prediction is used to adjust the marketing messages for each season and holidays by injecting specific part of speech using our short text generator and enhance the customer adhesion to marketing texts. |
| Schlagworte: | d3, Data intelligence, data mining, data visualization, KNIME, Linked Data, social media |
| Autoren | |
| Hinzugefügt von: | [] |
| Gesamtbewertung: | 5 |
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