Analyzing Opinions and Argumentation in News Editorials and Op-Eds

Analyzing opinions and arguments in news editorials and op-eds is an interesting and a challenging task. The challenges lie in multiple levels – the text has to be analyzed in the discourse level (paragraphs and above) and also in the lower levels (sentence, phrase and word levels). The abundance of implicit opinions involving sarcasm, irony and biases adds further complexity to the task. The available methods and techniques on sentiment analysis and opinion mining are still much focused in the lower levels, i.e., up to the sentence level. However, the given task requires the application of the concepts from a number of closely related sub-disciplines – Sentiment Analysis, Argumentation Theory, Discourse Analysis, Computational Linguistics, Logic and Reasoning etc. The primary argument of this paper is that partial solutions to the problem can be achieved by developing linguistic resources and using them for automatically annotating the texts for opinions and arguments. This paper discusses the ongoing efforts in the development of linguistic resources for annotating opinionated texts, which are useful in the analysis of opinions and arguments in news editorials and op-eds. Keywords—editorials; opinions; arguments; persuasion; sentiment analysis; annotation; NLP