Please use this identifier to cite or link to this item: https://www.um.edu.mt/library/oar/handle/123456789/74875
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dc.date.accessioned2021-04-28T07:33:16Z-
dc.date.available2021-04-28T07:33:16Z-
dc.date.issued2019-
dc.identifier.citationSpiteri, J.C. (2019). dOMiNiuM PubliCuM: opinion mining of news portal comments (Bachelor's dissertation).en_GB
dc.identifier.urihttps://www.um.edu.mt/library/oar/handle/123456789/74875-
dc.descriptionB.SC.ICT(HONS)ARTIFICIAL INTELLIGENCEen_GB
dc.description.abstractSentiment analysis is a research problem with great potential, given the enormous applications of being able to accurately summarise the opinion expressed by a person towards any topic. It has seen a lot of research into product reviews over the years, unfortunately research into sentiment analysis on more ambiguous data like news portal comments has been far more limited due to greater challenges. We propose a rules-based aspect level sentiment analysis using an opinion word corpus to detect the sentiment expressed towards entities in user-generated content, noted to be one of the more complex forms of data. The system is designed to use comments extracted from the Times of Malta news portal. Following the extraction of comments each sentence is processed to identify if it is in English or not to ensure that only English sentences are processed further. Sentences not in the English language are simply marked and stored. The English sentences within a comment are each processed for sentiment analysis. The scores from each sentence contribute to the sentiment mean value of each entity in the specific comment. Experimental results indicate that this approach looks promising for similar endeavours in the future.en_GB
dc.language.isoenen_GB
dc.rightsinfo:eu-repo/semantics/restrictedAccessen_GB
dc.subjectNews Web sites -- Maltaen_GB
dc.subjectTimes of Maltaen_GB
dc.subjectMass media and public opinion -- Maltaen_GB
dc.subjectSentiment analysis -- Maltaen_GB
dc.titledOMiNiuM PubliCuM : opinion mining of news portal commentsen_GB
dc.typebachelorThesisen_GB
dc.rights.holderThe copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder.en_GB
dc.publisher.institutionUniversity of Maltaen_GB
dc.publisher.departmentFaculty of Information and Communication Technology. Department of Artificial Intelligenceen_GB
dc.description.reviewedN/Aen_GB
dc.contributor.creatorSpiteri, Jean Claude (2019)-
Appears in Collections:Dissertations - FacICT - 2019
Dissertations - FacICTAI - 2019

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