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dc.contributor.advisorYang, Yimin
dc.contributor.advisorWei, Ruizhong
dc.contributor.authorZhao, Jingtian
dc.date.accessioned2023-06-29T16:01:13Z
dc.date.available2023-06-29T16:01:13Z
dc.date.created2023
dc.date.issued2023
dc.identifier.urihttps://knowledgecommons.lakeheadu.ca/handle/2453/5194
dc.description.abstractIn the past few decades, with the explosion of information, a large number of computer scientists have devoted themselves to analyzing collected data and applying these findings to many disciplines. Natural language processing (NLP) has been one of the most popular areas for data analysis and pattern recognition. A significantly large amount of data is obtained in text format due to the ease of access nowadays. Most modern techniques focus on exploring large sets of textual data to build forecasting models; they tend to ignore the importance of temporal information which is often the main ingredient to determine the performance of analysis, especially in the public policy view. The contribution of this paper is three-fold. First, a dataset called COVID-News is collected from three news agencies, which consists of article segments related to wearing masks during the COVID-19 pandemic. Second, we propose a long-short term memory (LSTM)-based learning model to predict the attitude of the articles from the three news agencies towards wearing a mask with both temporal and textural information. Then we added the BERT model to further improve and enhance the performance of the proposed model. Experimental results on the COVIDNews dataset show the effectiveness of the proposed LSTM-based algorithm.en_US
dc.language.isoen_USen_US
dc.subjectNatural Language Processing (NLP)en_US
dc.subjectArtificial Intelligence (AI)en_US
dc.subjectMachine learningen_US
dc.titleAdding time-series data to enhance performance of naural language processing tasksen_US
dc.typeThesisen_US
etd.degree.nameMaster of Scienceen_US
etd.degree.levelMasteren_US
etd.degree.disciplineComputer Scienceen_US
etd.degree.grantorLakehead Universityen_US


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