Sentiment analysis of product reviews using coupled sentence-bidirectional encode representations from transformers and topic modeling

In the research of product design, determining the product feature is the essential way to develop optimal business strategy, and the customer reviews on public websites are the valuable sources to reach the goal. The proposed research introduces three topic models and sentiment analysis using Sentence-BERT (S-BERT) technique in detail. Topic modeling stands as a pivotal technique in the domain of NLP (Natural Language Processing) and ML (Machine Learning), offering a systematic approach for uncovering hidden thematic structures within extensive collections of textual data. This unsupervised learning approach facilitates the optimization and organization of large datasets, enabling researchers and practitioners to identify patterns of topics across a corpus without large-scale manual labeling. S-BERT is a updated modification of BERT (Bidirectional Encoder Representations from Transformers) model which enhances its efficiency and effectiveness for generating sentence embeddings, where machines are allowed to understand human language in a numerical format precisely. The processes of data mining, model selection, statistical learning, and sentiment analysis are integrated and consolidated all at once in this proposed research. For the case study of well-known electronic products from online review, the proposed methodology regularizes the unstructured text data, and sets benchmarks for choosing a optimal model beforehand. Afterwards, it enables to capture the product keywords that customer pay much attention on with the help of topic modeling, and thereby the sentiment intensities of each product attribute are computed using advanced pre-trained language transformer, S-BERT. Therefore, the attributes with their satisfaction derived from customer review are automatically identified, which can facilitate the improvement of product development, and also clarify the guideline of quality control in an industry to solve the real-life problems.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Industrial Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2024

Author dc:creator

• Chao, Szumin

Contributors dc:contributor

• Kim, Harrison

• Product Design
• Product Review
• Topic Modeling
• Sentence-bert
• Text Transformer
• Data Mining
• Data-driven Design
• Optimization
• Latent Dirichlet Allocation
• Non-negative Matrix Factorization
• Latent Semantic Analysis
• Natural Language Processing
• Sentiment Analysis

Statement dc:rights

• Copyright 2024 Szumin Chao

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/124343

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F124343