Graduation Year
2017
Date of Submission
5-2017
Document Type
Open Access Senior Thesis
Degree Name
Bachelor of Arts
Department
Mathematics
Second Department
Computer Science
Reader 1
Blake Hunter
Terms of Use & License Information
Rights Information
© 2017 Alex A Waggoner
Abstract
Topic modeling refers to the process of algorithmically sorting documents into categories based on some common relationship between the documents. This common relationship between the documents is considered the “topic” of the documents. Sentiment analysis refers to the process of algorithmically sorting a document into a positive or negative category depending whether this document expresses a positive or negative opinion on its respective topic. In this paper, I consider the open problem of document classification into a topic category, as well as a sentiment category. This has a direct application to the retail industry where companies may want to scour the web in order to find documents (blogs, Amazon reviews, etc.) which both speak about their product, and give an opinion on their product (positive, negative or neutral). My solution to this problem uses a Non-negative Matrix Factorization (NMF) technique in order to determine the topic classifications of a document set, and further factors the matrix in order to discover the sentiment behind this category of product.
Recommended Citation
Waggoner, Alexander A., "Triple Non-negative Matrix Factorization Technique for Sentiment Analysis and Topic Modeling" (2017). CMC Senior Theses. 1550.
https://scholarship.claremont.edu/cmc_theses/1550
Included in
Numerical Analysis and Scientific Computing Commons, Other Applied Mathematics Commons, Other Mathematics Commons, Theory and Algorithms Commons
