Open Access Thesis
Date of Award
Sentiment analysis is a process of learning the relationship between sentiment label
and text. The research value of sentiment analysis is two-fold: first, it has a wide range
of applications in many sectors and industries, e.g., the industry has flourished due to the
proliferation of commercial applications such as using sentiment analysis as an integrated
part of customer experience strategy. Second, it offers an array of new challenging problems
for research community such as word feature embedding and machine learning. Albeit earlier
methods such as Naïve Bayes (NB), Random Forest (RF), k-Nearest-Neighbours (kNN),
Support Vector Machine (SVM) and more recent methods such as Deep Learning (DL)
methods are effective, they are primarily designed for shorter or longer textual data thus
are not able to maintain a robust performance across a variety of text with diverse lengths.
In reality, some text is as abbreviated as one single word while others are so pleonastic
that are over thousands of words. Moreover, ad hoc combination of feature embedding and
learning methods makes it more difficult to choose the right approach for different types of
textual data. Undoubtedly an integrated feature embedding and sentiment analysis method
is desirable. In this thesis, we introduce multi-way FM as a new method for sentiment analysis
accounting for higher-order feature interaction. We demonstrate the performance and
flexibility of the FM method to other competing methods by tuning a single parameter to
accommodate both shorter Twitter and longer movie review documents.
Zhang, Jingwei, "Multi-Way Factorization Machine For Sentiment Analysis" (2017). Wayne State University Theses. 599.