Computational Linguistic Model for Analyzing Opinionated Texts.

      Аннотация: This research work conducts a feasibility study of a complete system for analysis and synthesis of opinions and arguments via a Computational Linguistic Model that suggests appropriate techniques and methods for consolidating material from a number of editorials on a particular topic, taking into account the opinion orientations in these editorials. The resources required for the model are developed, which involves downloading and compiling editorials from a number of online news portals, constructing linguistic resources (lexicons, opinion patterns and contextual rules, semantic tagset), manually and automatically annotating the editorials with some crucial information like opinion orientation, opinion strength, persuasion effect, commitment level etc. Results have shown that that the task can be accomplished with reasonable accuracies. As the resources get more fine-tuned and attain wider coverage, the accuracies are expected to increase further. The development of a complete system and its evaluation has been left as future extensions to this work.

Ключевые слова: argumentation, Opinion Mining, Sentiment Analysis, Opinion Summarization, Corpus Tagging, Sentiment/Polarity Lexicon