Temporal and spatiotemporal investigation of tourist attraction visit sentiment on Twitter

Abstract

In this paper, we propose a sentiment-based approach to investigate the temporal and spatiotemporal effects on tourists’ emotions when visiting a city’s tourist destinations. Our approach consists of four steps: data collection and preprocessing from social media; visitor origin identification; visit sentiment identification; and temporal and spatiotemporal analysis. The temporal and spatiotemporal dimensions include day of the year, season of the year, day of the week, location sentiment progression, enjoyment measure, and multi-location sentiment progression. We apply this approach to the city of Chicago using over eight million tweets. Results show that seasonal weather, as well as special days and activities like concerts, impact tourists’ emotions. In addition, our analysis suggests that tourists experience greater levels of enjoyment in places such as observatories rather than zoos. Finally, we find that local and international visitors tend to convey negative sentiment when visiting more than one attraction in a day whereas the opposite holds for out of state visitors.

A Review of Text Corpus-Based Tourism Big Data Mining

“…Study [73] has confirmed that differences in tourist attributes can also lead to differences in tourist perceptions. Through the study of the Cape Town tourism market [144], it was found that visitors' age, place of residence, destination stay time, return visits, etc., had an important influence on the perception of tourists, and the sentiments they conveyed [145] also had different characteristics.…”

Social weather: A review of crowdsourcing‐assisted meteorological knowledge services through social cyberspace

“…In addition to meteorological data in this area, data from other fields are important sources of data supplementation, especially when the actual problem is a cross‐disciplinary task (e.g. tourism (Padilla et al , ), agriculture (Minet et al , ; Zipper, ), etc.). The advantages and limitations of different social weather reveal a huge demand for advanced data fusion approaches and techniques.…”

“…Second, although social weather utilizes the advantages of data fusion, fusing this large heterogeneous dataset efficiently and sparingly between meteorological agencies and other departments in various domains is difficult (e.g. transportation, agriculture and tourism) and is known as the so‐called ‘data islands’ problem (Dey et al , ; Zipper, ; Padilla et al , ). Third, the crowdsourcing data collection of social weather provides opportunities for more precise and individual‐level data collection, thereby relieving the dependency on deploying a large number of sensors which are costly to deploy over dense ground even‐space grids (Strangeways, ).…”

Modeling Spatiotemporal Factors Associated With Sentiment on Twitter: Synthesis and Suggestions for Improving the Identification of Localized Deviations

“…However, the results and conclusions vary from study to study, and these differences may depend on the methods used to aggregate sentiment across sets of tweets or users, differences in the ways the investigators sampled the data, differences in the sentiment analysis algorithms or tools used, or because of challenges associated with validating results against external information. In comparison, studies examining variation in sentiment by geography or weather are relatively rare compared with those that measure temporal variation [30,31,32,33,34]. Studies that report analyses for social interactions on Twitter-tweets that mention, reply to, or quote other users-do not appear to have focused on measuring differences in the sentiment relative to tweets that broadcast a message [35].…”