Perform sentiment analysis on LibQUAL+ comments using AlchemyAPI
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Perform sentiment analysis on LibQUAL+ comments using AlchemyAPI

Open-ended respondent comments are a valuable complement to the regular quantitative findings provided by most surveys. While reading every comment provides insight for the investigator, more systematic analysis and summarization of extensive qualitative feedback is a time-consuming task. Recent advances in sentiment analysis, harnessing natural language processing, text analysis, computational linguistics and machine deep learning have opened a rich seam of possibilities for the automation of a range of hitherto, human-only tasks.

The AlchemyAPI conveniently exposes the necessary computing power, algorithms and data via well-defined interfaces to allow us explore these possibilities.

This project is a recipe to:

  • Parse the textfile output of a set of LibQUAL+ comments (multiple files over multiple years are accommodated)
  • Submit each comment to the AlchemyAPI for both document and keyword level sentiment analysis
  • Store the LibQUAL+ data together with per comment, and per keyword sentiment scores in a SQlite database.
  • Additionally store Sentiment polarity and keyword relevance.
  • Provide a series of SQL statements and custom queries
  • Visualize the data overview: This program will perform document level sentiment analysis on your comments and store your results in Sqlite3 database for analysis. Dependencies:

  • The SQlite database availalbe from
  • The SQLite DBI driver DBD::SQLite - install using cpan DBD::SQLite
  • The AlchemyAPI Perl SDK available from (You'll need your own api key, also available from to run this)