Applied Sciences · 2024 · 11 citations · 34 references
BusinessMarketingMarketing ManagementConsumer BehaviorMarketing AnalyticsText AnalyticsCustomer Experience ManagementMobile CommerceOnline Consumer BehaviorOpinion MiningSentiment AnalysisTourism MarketingConsumer Behavior AnalysisConsumer Opinion MiningConsumer SentimentCustomer Emotion AnalysisMobile Consumer BehaviorMobile Tourism ApplicationsMobile Travel BookingUser Reviews
This study aims to analyse consumer experiences, purchase behaviours, and emotional responses through Booking and Expedia’s mobile applications. The 2000 user reviews collected from Google Play were subjected to a comprehensive sentiment analysis, text mining, and topic modelling process to identify the key elements that shape consumers’ emotional experiences and purchase decisions. According to the results of text mining and sentiment analysis performed with Python’s WordNet library, 81.9% of Booking.com reviews are positive, 8.4% are negative, and 11.3% are neutral, whereas 55.8% of Expedia reviews are positive, 37.8% are negative, and 8.0% are neutral. In the topic modelling analysis, Booking.com emphasised ease of booking, while Expedia emphasised difficulties in cancellation and refund processes. These findings provide valuable insights into how consumers’ emotional states and purchasing behaviours are reflected in their experiences with mobile applications. The study enables the development of strategic recommendations for marketing management to better analyse consumers’ expectations and experiences.
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An overview of the supervised machine learning methods
Vladimir Nasteski · HORIZONS B · 2017 · 667 citations
Word Cloud Explorer: Text Analytics Based on Word Clouds
Florian Heimerl, Steffen Lohmann, Simon de Lange et al. · 2014 · 541 citations