Affect analysis of communication texts in Turkish language

As digital technologies expanded our range of online communication, textual affect analysis become the focus of considerable interest in several disciplines. While many studies have been conducted in English language, there are only a few tools specific for the Turkish language. One reason for that is the lack of lexical resources for affect analysis and annotated corpora for an accurate evaluation. In this thesis, we develop an approach for continuous and dimensional affect analysis of Turkish communications by combining several tools. We conduct experiments on various text corpora in Turkish including online multi-party chat records, psychotherapy records, Twitter data, movie reviews, and teachers' comments on high school students. Analyzing such texts brings challenges like non-standard word usage, grammatical irregularities, abbreviation usage, and spelling mistakes. We propose several pre-processing steps to deal with these. Then, we adapt an affective word dictionary from English to Turkish, and by expanding it with synsets, obtain 15,200 words with annotations for valence, arousal, and dominance. We also employ a list of frequently used abbreviations, emoticons, interjections, modifiers (intensifiers and diminishers), and other linguistic indicators to capture the overall affective state at the sentence level. We recruit and train annotators to obtain affective ground truth specifically for multi-party chat records. Our results show that the proposed system is useful, yet there is much room for improvement at different stages.