Sentiment analysis in geo social streams by using machine learning technique

Detalhes bibliográficos
Autor(a) principal: Twanabasu, Bikesh
Data de Publicação: 2018
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/33797
Resumo: Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial Technologies
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spelling Sentiment analysis in geo social streams by using machine learning techniqueGeovisualizationMachine LearningOpinion MiningSentiment AnalysisDissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesMassive amounts of sentiment rich data are generated on social media in the form of Tweets, status updates, blog post, reviews, etc. Different people and organizations are using these user generated content for decision making. Symbolic techniques or Knowledge base approaches and Machine learning techniques are two main techniques used for analysis sentiments from text. The rapid increase in the volume of sentiment rich data on the web has resulted in an increased interaction among researchers regarding sentiment analysis and opinion (Kaushik & Mishra, 2014). However, limited research has been conducted considering location as another dimension along with the sentiment rich data. In this work, we analyze the sentiments of Geotweets, tweets containing latitude and longitude coordinates, and visualize the results in the form of a map in real time. We collect tweets from Twitter using its Streaming API, filtered by English language and location (bounding box). For those tweets which don’t have geographic coordinates, we geocode them using geocoder from GeoPy. Textblob, an open source library in python was used to calculate the sentiments of Geotweets. Map visualization was implemented using Leaflet. Plugins for clusters, heat maps and real-time have been used in this visualization. The visualization gives an insight of location sentiments.Ramos Romero, José FranciscoFernandez, Oscar BelmonteHenriques, Roberto André PereiraRUNTwanabasu, Bikesh2018-04-04T16:59:34Z2018-03-022018-03-02T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/33797TID:201894009enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-10T15:43:20ZPortal AgregadorONG
dc.title.none.fl_str_mv Sentiment analysis in geo social streams by using machine learning technique
title Sentiment analysis in geo social streams by using machine learning technique
spellingShingle Sentiment analysis in geo social streams by using machine learning technique
Twanabasu, Bikesh
Geovisualization
Machine Learning
Opinion Mining
Sentiment Analysis
title_short Sentiment analysis in geo social streams by using machine learning technique
title_full Sentiment analysis in geo social streams by using machine learning technique
title_fullStr Sentiment analysis in geo social streams by using machine learning technique
title_full_unstemmed Sentiment analysis in geo social streams by using machine learning technique
title_sort Sentiment analysis in geo social streams by using machine learning technique
author Twanabasu, Bikesh
author_facet Twanabasu, Bikesh
author_role author
dc.contributor.none.fl_str_mv Ramos Romero, José Francisco
Fernandez, Oscar Belmonte
Henriques, Roberto André Pereira
RUN
dc.contributor.author.fl_str_mv Twanabasu, Bikesh
dc.subject.por.fl_str_mv Geovisualization
Machine Learning
Opinion Mining
Sentiment Analysis
topic Geovisualization
Machine Learning
Opinion Mining
Sentiment Analysis
description Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial Technologies
publishDate 2018
dc.date.none.fl_str_mv 2018-04-04T16:59:34Z
2018-03-02
2018-03-02T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/33797
TID:201894009
url http://hdl.handle.net/10362/33797
identifier_str_mv TID:201894009
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
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collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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