Geospatial Forecasting and Social Media Exploration Based on Sentiment Analysis: Application to Flood Forecasting

2021 ◽  
pp. 19-29
Author(s):  
Sara Abas ◽  
Malika Addou

Since the emergence of the social media, many studies are conducted on social media to gain information on social media users. Among these studies are sentiment analysis which is an analysis of user sentiments and emotions towards an object, term, or event based on what they post. Sentiment analysis are often conducted on sites like Facebook and Twitter because of their huge number of users and popularity. This paper aims to create a GUI-based sentiment analysis application to find out popularity of universities based on Twitter user’s sentiment. For this purpose, we firstly collected 600 tweets datasets, which is a mixture of 200 tweets each from Princeton University, Stanford University and University of Oxford for a period of 4 days (12/1/2018 to 15/1/2018). Second, the tweets were classified based on their sentiment into “positive”, "neutral" and “negative” tweets. Finally, the results were being analyzed in terms of Precision, Recall and F1 score. These information will help universities to gather information of public sentiment towards their institution and allow them to recognize their strength and weakness. Universities can use that information to improve their public image if needed in the future.


2021 ◽  
Vol 13 (7) ◽  
pp. 3836
Author(s):  
David Flores-Ruiz ◽  
Adolfo Elizondo-Salto ◽  
María de la O. Barroso-González

This paper explores the role of social media in tourist sentiment analysis. To do this, it describes previous studies that have carried out tourist sentiment analysis using social media data, before analyzing changes in tourists’ sentiments and behaviors during the COVID-19 pandemic. In the case study, which focuses on Andalusia, the changes experienced by the tourism sector in the southern Spanish region as a result of the COVID-19 pandemic are assessed using the Andalusian Tourism Situation Survey (ECTA). This information is then compared with data obtained from a sentiment analysis based on the social network Twitter. On the basis of this comparative analysis, the paper concludes that it is possible to identify and classify tourists’ perceptions using sentiment analysis on a mass scale with the help of statistical software (RStudio and Knime). The sentiment analysis using Twitter data correlates with and is supplemented by information from the ECTA survey, with both analyses showing that tourists placed greater value on safety and preferred to travel individually to nearby, less crowded destinations since the pandemic began. Of the two analytical tools, sentiment analysis can be carried out on social media on a continuous basis and offers cost savings.


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