Date of Award
2019
Publisher
North Dakota State University
Document Type
Dissertation
Degree Awarded
Doctor of Philosophy (PhD)
Program
Statistics
Department
Statistics
College
Science and Mathematics
Faculty Advisor
Shen, Gang
Description
The task of opinion mining from product reviews has been achieved by employing rule-based approaches or generative learning models such as hidden Markov models (HMMs). This paper introduced a discriminative model using linear-chain Conditional Random Fields (CRFs) that can naturally incorporate arbitrary, non-independent features of the input without conditional independence among the features or distributional assumptions of inputs. The framework firstly performs part-of-speech (POS) tagging tasks over each word in sentences of review text. The performance is evaluated based on three criteria: precision, recall and F-score. The result shows that this approach is effective for this type of natural language processing (NLP) tasks. Then the framework extracts the keywords associated with each product feature and summarizes into concise lists that are simple and intuitive for people to read.
Recommended Citation
Ming, Yue, "A Conditional Random Field (CRF) Based Machine Learning Framework for Product Review Mining" (2019). Statistics. 27.
https://digitalcommons.ndsu.edu/stats/27
Author ORCID Identifier
0000-0002-7571-8274
Handle Identifier
https://hdl.handle.net/10365/29406