Sentiment analysis of Swedish social media: Using random indexing to improve cross-domain sentiment classification
Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Social media has grown extremely fast in recent years andin the vast number of posts being made everyday people expresstheir opinions about all kinds of topics. These opinionsare very valuable and there is a need for a way toautomatically identify and extract them. This is what sentimentanalysis is about but there are a number of issuesrelated to this task. In particular the large number anddiversity of the texts to analyze causes problems for ordinarymethods of natural language processing. In this thesisa method utilizing a technique called Random Indexing isproposed which tries to overcome some of the issues. Theconclusion is that the use of Random Indexing does aid insolving the problem but also that more work is needed inorder to have a fully satisfying solution.
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IdentifiersURN: urn:nbn:se:kth:diva-156249OAI: oai:DiVA.org:kth-156249DiVA: diva2:766075