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Title:HIBRIDNI PRISTOP ZA ZAZNAVO ELEMENTOV SUBJEKTIVNOSTI V BESEDILNIH TOKOVIH
Authors:Verlič, Mateja (Author)
Kokol, Peter (Mentor) More about this mentor... New window
Zorman, Milan (Co-mentor)
Files:.pdf DR_Verlic_Mateja_1979.pdf (2,68 MB)
 
Language:Slovenian
Work type:Dissertation (m)
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:Analiza razpoloženja je dokaj nova veja besedilnega rudarjenja, ki je zadnji čas še posebej priljubljena zaradi ogromnega potenciala za raziskovanje javnega mnenja na najrazličnejših področjih. Za razliko od priklica informacij in računalniške lingvistike, na katerih je osnovana, se analiza razpoloženja ne osredotoča na teme v dokumentu ali na objektivne informacije (o lokaciji, času,osebah), ampak na subjektivno mnenje, ki ga pisec izraža v dokumentu. Še posebej zanimiva je za analizo besedilnih tokov, ki so besedila s posebnimi lastnostmi dostopna na spletu. Obstaja veliko možnosti uporabe, na primer spremljanje mnenj o določenem produktu ali storitvi, spremljanje javnega mnenja o političnih kandidatih med volitvami ali o družbeno-političnih dogajanjih in podobno. Pri analizi razpoloženja ne gre le za zaznavanje mnenja, ampak tudi za izločitev ustreznih značilk, na podlagi katerih se mnenje opredeli kot pozitivno (dobro) ali negativno (slabo). V doktorski disertaciji smo raziskali polavtomatsko in avtomatsko prepoznavanje elementov subjektivnosti, ki so nosilci mnenja oziroma razpoloženja, na osnovi katerih smo zgradili slovarje za nadaljnjo klasifikacijo. Predstavili smo nov hibridni pristop h klasifikaciji razpoloženja in orodje, ki ta pristop implementira. V disertaciji smo združili ideje s področja strojnega učenja, priklica informacij, računalniške lingvistike in tudi s področja psihologije.
Keywords:analiza razpoloženja, rudarjenje mnenja, rudarjenje po besedilih, klasifikacija, strojno učenje
Year of publishing:2009
Publisher:[M. Verlič]
Source:Maribor
UDC:004.6:004.55(043.3)
COBISS_ID:13698838 Link is opened in a new window
Views:2056
Downloads:286
Metadata:XML RDF-CHPDL DC-XML DC-RDF
Categories:KTFMB - FERI
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Secondary language

Language:English
Title:Hybrid approach to detection of elements of subjectivity in text streams
Abstract:Sentiment analysis or opinion mining is relatively new branch of text mining and is very popular lately especially because of the potential for mining public opinion in different areas of interest. In comparison to Information retrieval and Computational Linguistics, opinion mining is not focused on topics or objective data (location, time, people) but on subjective opinion of the document writer. Sentiment analysis is especially interesting for analyzing text streams, which are texts with special characteristics available on the web. There are many possibilities of applying sentiment analysis, for example, it can be used to track opinions on some special product or service, tracking public opinion on political candidates during election time or on some political situations. Opinion mining is not only about determining opinions, but also to extract features that can be used to determine, whether opinion was positive (good) or negative (bad). This thesis describes we researched semi-automated and automated detection of subjective elements - terms bearing opinion or sentiment and which served as a basis for lexicons to be later used in classification. New hybrid method of sentiment classification has been proposed and implemented in our tool SentimentHitchhicker. Thesis combines ideas and methods from different fields, including machine learning, information retrieval, computer linguistics and psychology.
Keywords:sentiment analysis, opinion mining, text mining, machine learning


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