Graduation Semester and Year
Fall 2024
Language
English
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Civil Engineering
Department
Civil Engineering
First Advisor
Kyeong Rok Ryu
Second Advisor
June Young Park
Third Advisor
Mohsen Shahandashti
Fourth Advisor
Joowon Im
Abstract
Infrastructure projects impact a broad range of stakeholders, particularly local communities, whose engagement is critical for successful outcomes. Despite the importance of public engagement in these projects, traditional methods of capturing and analyzing public opinion often fail to fully represent the diverse, genuine perspectives involved. This has led to conflicts between community members and project sponsors. On the other hand, despite advancements in text analytics, including natural language processing (NLP) and its subfields such as topic modeling, sentiment analysis, and neural networks, their functionalities and effectiveness in analyzing public opinion in the domain of infrastructure projects have not been fully investigated.
To address these challenges, this study aims to streamline public engagement in urban transportation infrastructure projects by applying NLP techniques to various communication sources, focusing on the North Houston Highway Improvement Project (NHHIP) case study. Particular emphasis is placed on (1) investigating the spectrum of public opinions on urban infrastructure through online and in-person channels by performing exploratory data analysis on the collected data, (2) conducting a comparative analysis of the performance and effectiveness of sentiment analysis and topic modeling methods across diverse sources and extracting static and temporal insights, and (3) developing a graph attention network for the relation extraction and classification of public concerns and their association with project phases.
The first objective extracts thematic insights and compares results using term-frequency analysis, text summarization, and temporal analysis, revealing an evolving trend in public awareness from general concerns to specific issues as well as the distinct nature of communication channels in representing various public concerns. The second objective evaluates the performance of Vader, Affin, TextBlob, DistilBERT, and RoBERTa for sentiment analysis, and LDA, NMF, and BERTopic for topic modeling on various sources of communication. The results show that traditional lexicon-based methods fail to accurately identify topics and sentiments in public opinion sources. However, RoBERTa excels in sentiment analysis, while BERTopic outperforms traditional methods in topic modeling due to its contextual and semantic capabilities. Finally, a Graph Attention Network (GAT) is developed to identify public concerns and associated project phases, outperforming the Convolutional Neural Network (CNN).
Keywords
Infrastructure, Transportation, Public Engagement, Public Opinion Analysis, Text Analytics, Natural Language Processing, Text Mining, Sentiment Analysis, Topic Modeling, Graph Attention Networks
Disciplines
Civil Engineering | Communication Technology and New Media | Computational Engineering | Computational Linguistics | Computer and Systems Architecture | Construction Engineering and Management | Digital Communications and Networking | Social Justice | Transportation Engineering | Urban Studies and Planning
License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Recommended Citation
Shamshiri, Alireza, "STREAMLINING PUBLIC ENGAGEMENT IN TRANSPORTATION PROJECTS USING TEXT ANALYTICS" (2024). Civil Engineering Dissertations. 504.
https://mavmatrix.uta.edu/civilengineering_dissertations/504
Included in
Civil Engineering Commons, Communication Technology and New Media Commons, Computational Engineering Commons, Computational Linguistics Commons, Computer and Systems Architecture Commons, Construction Engineering and Management Commons, Digital Communications and Networking Commons, Social Justice Commons, Transportation Engineering Commons, Urban Studies and Planning Commons