Book Classification Via Fuzzy Logic Jeremy Keer

1. Book Classification Via Fuzzy Logic Jeremy Keer

2. Project Goals • Develop a fuzzy logic rule set to classify the content of fantasy and science fiction books based upon genre and hardness • Allow a user to classify a book with cursory knowledge with the purpose of finding whether it is similar to other styles they have enjoyed • If possible, use clustering ANN fora visual output

3. Why Use Fuzzy Logic? • Line between genres can be blurry at times • Existing deeper classification systems tend to rely on tags that ultimately don’t offer much information on the actual content of the story

4. Data Set Generation • Data is gathered via questionnaires • Questions must be formulated such that answers can be given reasonably in the form of a scale • Two types of data sets used: • Shallow set answerable based largely on summary and tags • Deep set meant to be answered bysomeone who has read the bookso that it can be accurately beclassified

5. Classification Methods • Data is analyzed and given values on two spectrums: • Science Fiction vs Fantasy: while sometimes seeming to be a binary choice, tends to actually be much more complex • Hardness: hardest is “real life,” becomes progressively softer as more fantastical elements are included

6. Expected Results • Questions work well, but would like to add more to improve classification • Finding questions that fit well to scales is the major problem • Reducing output regions of rule set should work for classification • For example, three regions on the SF/Fantasy scale and five regions foreach of these for hardness