Tourism destination management using sentiment analysis and geo-location information: a deep learning approach

Marina Paolanti (), Adriano Mancini, Emanuele Frontoni, Andrea Felicetti, Luca Marinelli, Ernesto Marcheggiani and Roberto Pierdicca
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Marina Paolanti: Università Politecnica delle Marche
Adriano Mancini: Università Politecnica delle Marche
Emanuele Frontoni: Università Politecnica delle Marche
Andrea Felicetti: Università Politecnica delle Marche
Luca Marinelli: Università Politecnica delle Marche
Ernesto Marcheggiani: Università Politecnica delle Marche
Roberto Pierdicca: Università Politecnica delle Marche

Information Technology & Tourism, 2021, vol. 23, issue 2, No 6, 264 pages

Abstract: Abstract Sentiment analysis on social media such as Twitter is a challenging task given the data characteristics such as the length, spelling errors, abbreviations, and special characters. Social media sentiment analysis is also a fundamental issue with many applications. With particular regard of the tourism sector, where the characterization of fluxes is a vital issue, the sources of geotagged information have already proven to be promising for tourism-related geographic research. The paper introduces an approach to estimate the sentiment related to Cilento’s, a well known tourism venue in Southern Italy. A newly collected dataset of tweets related to tourism is at the base of our method. We aim at demonstrating and testing a deep learning social geodata framework to characterize spatial, temporal and demographic tourist flows across the vast of territory this rural touristic region and along its coasts. We have applied four specially trained Deep Neural Networks to identify and assess the sentiment, two word-level and two character-based, respectively. In contrast to many existing datasets, the actual sentiment carried by texts or hashtags is not automatically assessed in our approach. We manually annotated the whole set to get to a higher dataset quality in terms of accuracy, proving the effectiveness of our method. Moreover, the geographical coding labelling each information, allow for fitting the inferred sentiments with their geographical location, obtaining an even more nuanced content analysis of the semantic meaning.

Keywords: Sentiment analysis; Geotagged social media; Deep learning; Tourism (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (6)

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DOI: 10.1007/s40558-021-00196-4

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