Sentiment classification of Swedish Twitter data

Abstract: Sentiment analysis is a field within the area of natural language processing that studies the sentiment of human written text. Within sentiment analysis, sentiment classification is a research area that has been of growing interest since the advent of digital social-media platforms, concerned with the classification of the subjective information in text data. Many studies have been conducted on sentiment classification, producing numerous of openly available tools and resources that further advance research, though almost exclusively for the English language. There are very few openly available Swedish resources that aid research, and sentiment classification research in non-English languages most often use English resources one way or another. The lack of non-English resources impedes research in other languages and there is very little research on sentiment classification using Swedish resources. This thesis addresses the lack of knowledge in this area by designing and implementing a sentiment classifier using Swedish resources, in order to evaluate how methods and best practices commonly used in English research transfer to Swedish. The results in this thesis indicate that Swedish resources can be used in the construction of internationally competitive sentiment classifiers and that methods commonly used in English research for pre- processing text data may not be optimal for the Swedish language.