Brand sentiment analysis of the Norwegian banking sector
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For the last two decades, the world wide web has become a social arena where people can express themselves. People write opinionated texts on social media towards various targets, which is read by other people. The readers are often influenced by what is written. Companies holding brands often keep a close eye on what people write about them, but it is a burdensome task to monitor the World Wide Web. The use of sentiment analysis for automatically analysing brands has been on the rise in recent years for this reason. Norwegian banks are increasingly considering themselves as brands, and they monitor their online reputation accordingly. This master thesis describe a system performing sentiment analysis on Norwegian reviews of the banking sector. The system use three different classifiers, Naive Bayes, Support Vector Machines and Maximum Entropy, to classify the polarity of reviews in the Norwegian banking sector. A custom made mapping between a Norwegian wordnet and the sentiment lexicon SentiWordNet aids the sentiment analysis with cross-lingual lookup, as Norwegian sentiment resources are limited. A set of unigrams and bigrams for the banking domain is also used as input to the classifiers, as well as textual features. The system also utilize knowledge of semantic structures in an attempt to extract sentiments on sub-aspects mentioned in the reviews.