Travel agency recommender system based on social media sentiment analysis

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dc.contributor.authorKing'ori, S. W.
dc.date.accessioned2023-10-03T09:26:42Z
dc.date.available2023-10-03T09:26:42Z
dc.date.issued2023
dc.descriptionFull- text thesis
dc.description.abstractIn today's highly competitive e-tourism industry, online reviews and recommendations are crucial to customers' travel decisions. This study focuses on reviewing the level of service quality in the e-tourism sector through social media sentiment analysis, aiming to aid travelers in making informed decisions about their travel arrangements through a recommender system. While other methods exist for recommender systems, they are not sufficient for the specific context of Kenya. To address this, the researcher develops a tool capable of providing personalized recommendations based on user destinations using the transformed data received from sentiment analysis. The study adopts an exploratory research design, targeting individuals who engage in social media discussions related to the e-tourism industry, including travelers, travel companies, and other tourists which is supported by Agile Development Methodology. The collected data is retrieved from Twitter, a prominent communication platform for travel agencies. The study utilizes SnScrape for tweet extraction and employs data preprocessing techniques to categorize and analyze the collected data using the Vader lexicon model. The collected data undergoes sentiment analysis, where each evaluated tweet is assigned a polarity tag indicating whether it is positive, negative, or neutral sentiment, with the analyzed results presented using charts and tables. Machine learning algorithms play a crucial role in providing personalized and relevant recommendations to users based on their destination preferences and historical data. K-Nearest neighbor, Support Vector Machine, and Naive Bayes are explored and evaluated to show the best-performing algorithm for the recommending system. A hybrid filtering approach is incorporated using content-based and collaborative filtering to create travel profiles based on the selected Reliability and validity measures are applied to ensure research quality, e research quality, both reliability and validity measures are applied which exhibit high levels of accuracy, precision, and F1 scores indicating their effectiveness in recommending travel agencies. Streamlit is used to build an interface and deploy the machine learning models using a set of rules to recommend travel agencies based on the user’s destination. The study concludes that recommender systems rely on feedback and online reviews shared by customers after traveling to various destinations. It recommends that travel providers acknowledge this trend and actively encourage customers to share their experiences through social media and other platforms. Additionally, the study suggests that users of sentiment analysis tools ensure diverse training data to mitigate bias and accurately reflect the sentiment of the target audience. Keywords: Service Quality, Sentiment Analysis, Travel Agencies, Recommender System, Twitter, Machine learning
dc.identifier.citationKing’ori, S. W. (2023). Travel agency recommender system based on social media sentiment analysis [Strathmore University]. http://hdl.handle.net/11071/13520
dc.identifier.urihttp://hdl.handle.net/11071/13520
dc.language.isoen
dc.publisherStrathmore University
dc.titleTravel agency recommender system based on social media sentiment analysis
dc.typeThesis
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