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An Opinion Analysis: What Do Customers Say About Hotel Ratings and Reviews in Malaysia?

Jupyter Notebook 99.11% Python 0.78% R 0.11%
data-analysis data-science exploratory-data-analysis hotel-data-analysis machine-learning opinion-mining sentiment-analysis supervised-learning unsupervised-learning data-visualization powerbi python r-programming sas adaboost bagging-ensemble classification decision-tree naive-bayes-classifier random-forest

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bachelordegree-capstone

Sunway University Business/Data Analytics Capstone Project 2021 (Individual)

An Opinion Analysis: What Do Customers Say About Hotel Ratings and Reviews in Malaysia?

Abstract. Since the spectacular growth of social media, online booking plat-forms, and blogs has become the standard in the digital age, businesses and or-ganizations have used data analytics to solve and boost business performance and improve overall decision-making. Online hotel booking and review websites have grown in popularity where consumers post online reviews, ratings, recom-mendations, pricing comparisons, and other factors that are important in many customer-centric businesses. In Malaysia, the hotel industry is a major contribu-tor to the country's economic prosperity. A hotel's primary goal is to satisfy cus-tomers, deliver high-quality service, and provide guests a memorable experience while staying at the hotel. Therefore, the purpose of this research is to identify the key topics and discover important insights which may influence customer satisfaction based on the hotel ratings and reviews. The study also analyzed the sentimental value as well as determining the performance accuracy from the hotel reviews. Opinion Mining, a combination of text and sentiment analysis, will be implemented to analyze the data collected from online booking and review web-site, TripAdvisor. The results will discuss the factors influencing customer satis-faction and the sentimental values from the hotel ratings and reviews using text analysis, corpus-based lexicon and supervised Machine Learning (ML) sentiment analysis.

Keywords: Opinion Mining, Text Analysis, Machine Learning, Hotel Reviews, Customer Satisfaction.

Content

  • PowerBI: Data Visualization & Dashboarding
  • Python: Data Exploration, Preprocessing (Cleaning), Analysis, and Visualization - Text & Sentiment Analysis and Supervised Machine Learning (Classification)
  • R: Data Exploration, Preprocessing (Cleaning), Analysis, and Visualization - Text & Sentiment Analysis
  • SAS (E-Miner): Text Parsing, Text Filtering, Concept Link, Text Cluster, and Text Topic - Text Analysis and Unsupervised Machine Learning

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