Word polarity detection using a multilingual approach

Determining polarity of words is an important task in sentiment analysis with applications in several areas such as text categorization and review analysis. In this thesis, we propose a multilingual approach for word polarity detection. We construct a word relatedness graph by using the relations in WordNet of a given language. We extend the graph by connecting the WordNets of different languages with the help of the Inter-Lingual-Index based on English WordNet. We develop a semi-automated procedure to produce a set of positive and negative seed words for foreign languages by using a set of English seed words. In our approach, these seed words are used for semi-supervised learning and for evaluation purposes. To identify the polarity of unlabeled words, we propose a method based on random walk model with commute time metric as proximity measure. We evaluate our multilingual approach for English and Turkish and show that it leads to improvement in performance for both languages. To the best of our knowledge, we report the first word polarity detection results for Turkish.