Possible directions for a new era of space activities

Collins, Patrick; Ohkami, Yoshiaki; Maita, Masataka

doi:10.2514/6.1993-5011

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…Social media has become a powerful hub of information around the world. Classical studies have based conclusions on surveys that have not been much instrumental in understanding the future of space tourism (Collins, 1994a(Collins, , 1994b(Collins, , 1995(Collins, , 1996O'Neil, 1998), and others have followed to assess only limited interests and desires and, unable to tap the future behaviour (Futron Corp, 2002).…”

Section: Introductionmentioning

confidence: 99%

“I need some space!” deciphering space tourism discussions on social media

Gulati

2022

GKMC

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Purpose Space tourism is fairly neglected in academic research and requires further exploration. Public reaction on social media offers great insights to understand the patterns of behaviour but is often ignored as a potential data source. Thus, this study aims to fill the gap by add to the literature on space tourism, social media analytics and behaviour. Design/methodology/approach The study adopts a qualitative approach and uses Twitter data for drawing conclusions. An exploratory design is used by analysing 10,000 tweets through unsupervised machine learning and two sets of analysis were conducted. First, sentiment analysis is performed using NRC Emotion Lexicon, which classifies the data as per eight basic emotions and polarity as positive and negative. The findings are complemented with a comparison cloud. Second, LDA Topic modelling using Gibbs Method is used to find ten broad topics that are used for discussions in space tourism tweets. Data visualisation technique is used to depict results using R language on RStudio. Findings A total of 21,784 emotions have tapped using the NRC Emotion Lexicon. Results indicate the dominance of positive sentiments (25%) with it surpassing the negative sentiments by many folds. The top emotions include trust and anticipation. The LDA-based Topic modelling identified seven correlated topic models that have been grouped by the author as space tourism in media, aspirations, ethical issues, criticism, descriptive, symbolism and miscellaneous. Originality/value To the best of the author’s knowledge, no study has attempted to study the response of space tourism on social media by tapping discussions in the form of Tweets. Thus, this study adds extensively and acts as a preliminary investigation on the public sentiments of space tourism on social media.

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Section: Introductionmentioning

confidence: 99%