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Articles 1 - 4 of 4
Full-Text Articles in Computational Linguistics
Bridging Nlp With Environmental Management By Sentiment Analysis And Topic Modeling On Offshore Wind Energy, Isabele Mansur Costa Bittencourt
Bridging Nlp With Environmental Management By Sentiment Analysis And Topic Modeling On Offshore Wind Energy, Isabele Mansur Costa Bittencourt
Theses, Dissertations and Culminating Projects
Offshore wind energy, which uses the power of winds on the seas to generate electricity, has emerged as a promising renewable energy source and an alternative for low-carbon future markets, especially during the climate crisis the world has been experiencing. However, it still raises controversial thoughts between people. Furthermore, amid the growth of the offshore wind energy market, the investigation of related topics, such as its impacts on wildlife, has become very common. Thus, this master’s thesis delves into the analysis of public and media opinion towards offshore wind energy. We use natural language processing techniques and pre-trained rule and …
Streamlining Public Engagement In Transportation Projects Using Text Analytics, Alireza Shamshiri
Streamlining Public Engagement In Transportation Projects Using Text Analytics, Alireza Shamshiri
Civil Engineering Dissertations - Archive
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 …
Predicting High-Cap Tech Stock Polarity: A Combined Approach Using Support Vector Machines And Bidirectional Encoders From Transformers, Ian L. Grisham
Predicting High-Cap Tech Stock Polarity: A Combined Approach Using Support Vector Machines And Bidirectional Encoders From Transformers, Ian L. Grisham
Electronic Theses and Dissertations
The abundance, accessibility, and scale of data have engendered an era where machine learning can quickly and accurately solve complex problems, identify complicated patterns, and uncover intricate trends. One research area where many have applied these techniques is the stock market. Yet, financial domains are influenced by many factors and are notoriously difficult to predict due to their volatile and multivariate behavior. However, the literature indicates that public sentiment data may exhibit significant predictive qualities and improve a model’s ability to predict intricate trends. In this study, momentum SVM classification accuracy was compared between datasets that did and did not …
A Study On The Efficacy Of Sentiment Analysis In Author Attribution, Michael J. Schneider
A Study On The Efficacy Of Sentiment Analysis In Author Attribution, Michael J. Schneider
Electronic Theses and Dissertations
The field of authorship attribution seeks to characterize an author’s writing style well enough to determine whether he or she has written a text of interest. One subfield of authorship attribution, stylometry, seeks to find the necessary literary attributes to quantify an author’s writing style. The research presented here sought to determine the efficacy of sentiment analysis as a new stylometric feature, by comparing its performance in attributing authorship against the performance of traditional stylometric features. Experimentation, with a corpus of sci-fi texts, found sentiment analysis to have a much lower performance in assigning authorship than the traditional stylometric features.