Group03 proposal
Contents
Overview/Motivation
Singapore Management University (SMU) has 2 physical libraries – the Li Ka Shing Library and Kua Geok Chee Library, which aim to provide plethora of information to drive intellectual exchange and creation of knowledge in the SMU community. Every 2 years, the SMU library will conduct a user survey of faculty, staff and students to collect information to gauge its performance in providing library services to the community, based on 4 categories of assessment:
Communication
Service delivery
Facilities and equipment
Information resources
Current reports generated from responses from the Library Survey in 2018 are mainly displays of survey responses with highlighting of what library users consider as most important, with aggregate statistical results generated, but we feel there is potential for more insight to be discovered from the data.
Our project hopes to create a R Shiny application in order to revisit and uncover insights from Singapore Management University (SMU) Library Survey 2018 data to uncover insights in the perception of importance of aspects of library services and facilities, as well as the library performance based on these matrices, based on inputs from faculty, staff and students. As the survey questions are being reused for the SMU Library Survey 2020, there is potential for re-use of the R Shiny application for this year.
Project Objectives
Exploratory data analysis in order to generate meaningful insights beyond aggregated statistical data and uncovering the factors considered important by library users.
Using text analytical techniques and topic modelling to analyses free text responses and sentiments of survey responders
Use latent class analysis and association & network analysis to determine relationships between factors used to benchmark performance and provision of library facilities.
Data Source & Inspiration References
Proposed Story/Dashboard
Libraries typically develop surveys for 3 reasons: to gauge user satisfaction, to assess users' needs (usage), or to learn more about outcomes—that is, the end results of using the library. A fourth purpose of surveys is to gather demographic information about library users. The purpose of our project is in line with these needs, where we will build broad visualizations, identify areas to delve into and propose solutions/better outcomes.
Data Preparation – Study the dataset provided and clean the data for conducting analysis of the Survey Results
Analysis of Survey - Deep dive into the survey
Exploratory Data Analysis - Perform EDA to analyze the survey results and come up with some interesting findings.
Sentiment Analysis- Analysis of the overall sentiment of the comments provided in the survey, leading to positive/negative emotions about the library
Model Building- Build a model using LCA / Neural Networks to understand which factors are more important to people from different study areas
Visualizations – Create Visuals to demonstrate the findings of the Survey
Implementation – Build R-Shiny app and provide technical report
Project Timeline
Data Description
Data Field
Description
Data Type
ResponseID
ID of the respondent
Numeric
Campus
Name of the Library
Categorical
Position
Designation
Categorical
StudyArea
Major area of study, research or teaching
Categorical
ID
Whether an International (non-exchange) student or not
Categorical
I01-I26
Survey Questions Category 1
Ordinal
P01-P26
Survey Questions Category 2
Ordinal
Comment1
Suggestions for improvement or any other comments about the Library
String
HowOftenL
How frequently the library is visited
Ordinal
HowOftenC
How frequently the campus is visited
Ordinal
HowOftenW
How frequently the library resources are accessed
Ordinal
NA01-NA26
Survery Questions Category 3
Ordinal
NPS1
Likelyhood of recommending the library service to other students
Ordinal
Tools & Packages
Tools used - Rstudio: https://rstudio.com/
Packages
Purpose
Shiny()
Package for produce their R shiny interface
Topicmodels()
Package for Topic Modelling
Ggplot2()
Package for produce their charts and visualizations
Dplyr()
Package for data manipulation.
SentimentAnalysis()
Package for Performing Sentiment Analysis on Remarks
WordCloud()
Package for Creating Wordclouds
Members – Milestones
Joshua Lam Jie Feng
Exploratory Data Analysis
Network Analysis
Karthik Nityanand
Exploratory Data Analysis
Latent Class Analysis
Shreyansh Shivam
Exploratory Data Analysis
Topic Modelling
References
https://www.lrs.org/library-user-surveys-on-the-web/
https://greenhill-library.org/wp-content/uploads/2016/12/public-survey-analysis-2016.pdf
