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Mapintel: enhancing competitive intelligence acquisition through embeddings and visual analytics

2022-05-18Dissertação de mestrado Acesso aberto
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dc.contributor.advisorBação, Fernando José Ferreira Lucas
dc.contributor.authorSilva, David Fontes Henriques Silvestre da
dc.date.accessioned2022-06-27T13:34:57Z
dc.date.available2022-06-27T13:34:57Z
dc.date.issued2022-05-18
dc.descriptionDissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analyticspt_PT
dc.descriptionThis work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project MapIntel (DSAIPA/DS/0116/2019): https://doi.org/10.54499/DSAIPA/DS/0116/2019
dc.description.abstractCompetitive Intelligence allows an organization to keep up with market trends and foresee business opportunities. This practice is mostly performed by analysts scanning for any piece of valuable information in a myriad of dispersed and unstructured sources. Here we present MapIntel, a system for acquiring intelligence from large collections of text data by representing each document as a multidimensional vector that captures its own semantics. The system is designed to handle complex Natural Language queries and visual exploration of the corpus, potentially aiding overburdened analysts in finding meaningful insights to help decision-making. The searching module of the system uses a retriever and re-ranker engine that first finds the closest neighbors to the query embedding, and then sifts the results through a cross-encoder model that identifies the most relevant documents. The browsing or visualization module also leverages the embeddings by projecting them onto 2 dimensions while preserving the multidimensional landscape, resulting in a map where semantically related documents form topical clusters which we capture using topic modeling. This map aims at promoting a fast overview of the corpus while allowing a more detailed exploration and interactive information encountering process. We evaluate the system and its components on the 20 newsgroups dataset, making use of the semantic document labels provided, and we demonstrate the superiority of Transformer-based components. Finally, we present a prototype of the system in Python and show how some of its features can be used to acquire intelligence from a news article corpus we collected during a period of 8 months.pt_PT
dc.identifier.tid203028414pt_PT
dc.identifier.urihttp://hdl.handle.net/10362/140849
dc.language.isoengpt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectNatural Language Processingpt_PT
dc.subjectTransformer Architecturept_PT
dc.subjectUMAPpt_PT
dc.subjectInformation Encounteringpt_PT
dc.subjectInformation Retrievalpt_PT
dc.subjectCompetitive Intelligencept_PT
dc.subjectSentence Embeddingspt_PT
dc.subjectTopic Modelingpt_PT
dc.subjectUnsupervised Learningpt_PT
dc.titleMapintel: enhancing competitive intelligence acquisition through embeddings and visual analyticspt_PT
dc.typemaster thesis
dspace.entity.typePublication
rcaap.rightsopenAccesspt_PT
rcaap.typemasterThesispt_PT
thesis.degree.nameMestrado em Ciência de Dados e Métodos Analíticos Avançados, especialização em Ciência de Dadospt_PT

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NIMS - Dissertações de Mestrado em Ciência de Dados e Métodos Analíticos Avançados (Data Science and Advanced Analytics)