A Visual Exploration Tool forTemporal Analysis of CustomerReviews

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Abstract

This thesis explores textual review data and how it changes over time. The thesisis motivated by the constantly generated textual reviews. Review sites like Yelp andTripAdvisor are generating hundreds of thousands of reviews monthly. Analysing thisamount of data is impossible by simply reading every individual review. We look forways to answer questions that business analysts, business owners, and investors ask aboutcustomer review data. This thesis asks questions such as: Why do review scores andtopics change over time? What are the major topics people discuss? What are the typicalreasons why review scores suddenly increase or decrease? What are topics that invokepermanent or transient changes in a large collection of review scores?We created a tool called Review Watcher, which provides novel approaches to examineand analyse review changes over time. The tool aims to provide simple, easily accessibleinformation regarding temporal changes in a collection of restaurant reviews. The tooluses real data provided by Yelp. It employs graphical ways to indicate changes in reviewscores over different periods of time. The tool analyses the review scores over time, andit tries to explain changes in these scores based on the textual content of the reviews.The tool utilises automated text processing algorithms to highlight important and oftenused words in text corpora.We used a qualitative evaluation to determine how well the tool manages to answer theresearch questions. We completed a user study with experts in the field of economics.They shared the insights they gathered using Review Watcher and compared them totheir experiences working with other tools for customer satisfaction and review analysis.As a result of our research, we show that Review Watcher manages to provide betterinsight into what are major topics in a collection of textual reviews. In the thesis, we showthat Review Watcher is better suited to highlighting review changes occurring over timeand giving insights to why the changes occurred, compared to existing tools for reviewexploration. The tool is also proving capable of handling millions of textual reviews oftens of thousands of restaurants with acceptable loading times for the user. The userstudy also reveals some of the tool’s limitations and potential for future work, for examplein introducing improved categorisation functions and geographical information about restaurants.

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BibTeX

@mastersthesis{Panayotov,
  title =      " A Visual Exploration Tool forTemporal Analysis of
               CustomerReviews",
  author =     "Blagoy Panayotov",
  year =       "2020",
  abstract =   "This thesis explores textual review data and how it changes
               over time.  The thesisis motivated by the constantly
               generated textual reviews. Review sites like Yelp
               andTripAdvisor are generating hundreds of thousands of
               reviews monthly. Analysing thisamount of data is impossible
               by simply reading every individual review. We look forways
               to answer questions that business analysts, business owners,
               and investors ask aboutcustomer review data. This thesis
               asks questions such as: Why do review scores andtopics
               change over time? What are the major topics people discuss?
               What are the typicalreasons why review scores suddenly
               increase or decrease? What are topics that invokepermanent
               or transient changes in a large collection of review
               scores?We created a tool called Review Watcher, which
               provides novel approaches to examineand analyse review
               changes over time. The tool aims to provide simple, easily
               accessibleinformation regarding temporal changes in a
               collection of restaurant reviews. The tooluses real data
               provided by Yelp. It employs graphical ways to indicate
               changes in reviewscores over different periods of time. The
               tool analyses the review scores over time, andit tries to
               explain changes in these scores based on the textual content
               of the reviews.The tool utilises automated text processing
               algorithms to highlight important and oftenused words in
               text corpora.We used a qualitative evaluation to determine
               how well the tool manages to answer theresearch questions.
               We completed a user study with experts in the field of
               economics.They shared the insights they gathered using
               Review Watcher and compared them totheir experiences working
               with other tools for customer satisfaction and review
               analysis.As a result of our research, we show that Review
               Watcher manages to provide betterinsight into what are major
               topics in a collection of textual reviews. In the thesis, we
               showthat Review Watcher is better suited to highlighting
               review changes occurring over timeand giving insights to why
               the changes occurred, compared to existing tools for
               reviewexploration. The tool is also proving capable of
               handling millions of textual reviews oftens of thousands of
               restaurants with acceptable loading times for the user. The
               userstudy also reveals some of the tool’s limitations and
               potential for future work, for examplein introducing
               improved categorisation functions and geographical
               information about restaurants.",
  month =      may,
  address =    "Favoritenstrasse 9-11/E193-02, A-1040 Vienna, Austria",
  school =     "Research Unit of Computer Graphics, Institute of Visual
               Computing and Human-Centered Technology, Faculty of
               Informatics, TU Wien ",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2020/Panayotov/",
}