What patterns were extracted which were correlated with emotional arguments?
Pattrn based on the speaker's own beliefs or claims, perhaps assuming that they themselves are credible or they involve assessment or evaluations of the arguments of the other speaker.  They are typically also very creative and diverse.
We investigate the characteristics of factual and emotional argumentation styles observed in online debates. Using an annotated set of"factual"and"feeling"debate forum posts, we extract patterns that are highly correlated with factual and emotional arguments, and then apply a bootstrapping methodology to find new patterns in a larger pool of unannotated forum posts. This process automatically produces a large set of patterns representing linguistic expressions that are highly correlated with factual and emotional language. Finally, we analyze the most discriminating patterns to better understand the defining characteristics of factual and emotional arguments.
Human lives are being lived online in transformative ways: people can now ask questions, solve problems, share opinions, or discuss current events with anyone they want, at any time, in any location, on any topic. The purposes of these exchanges are varied, but a significant fraction of them are argumentative, ranging from hot-button political controversies (e.g., national health care) to religious interpretation (e.g., Biblical exegesis). And while the study of the structure of arguments has a long lineage in psychology BIBREF0 and rhetoric BIBREF1 , large shared corpora of natural informal argumentative dialogues have only recently become available. Natural informal dialogues exhibit a much broader range of argumentative styles than found in traditional work on argumentation BIBREF2 , BIBREF0 , BIBREF3 , BIBREF4 . Recent work has begun to model different aspects of these natural informal arguments, with tasks including stance classification BIBREF5 , BIBREF6 , argument summarization BIBREF7 , sarcasm detection BIBREF8 , and work on the detailed structure of arguments BIBREF9 , BIBREF10 , BIBREF11 . Successful models of these tasks have many possible applications in sentiment detection, automatic summarization, argumentative agents BIBREF12 , and in systems that support human argumentative behavior BIBREF13 . Our research examines factual versus feeling argument styles, drawing on annotations provided in the Internet Argument Corpus (IAC) BIBREF6 . This corpus includes quote-response pairs that were manually annotated with respect to whether the response is primarily a factual or feeling based argument, as Section SECREF2 describes in more detail. Figure FIGREF1 provides examples of responses in the IAC (paired with preceding quotes to provide context), along with the response's factual vs. feeling label. factual responses may try to bolster their argument by providing statistics related to a position, giving historical or scientific background, or presenting specific examples or data. There is clearly a relationship between a proposition being factual versus objective or veridical, although each of these different labelling tasks may elicit differences from annotators BIBREF14 , BIBREF15 , BIBREF16 , BIBREF17 . The feeling responses may seem to lack argumentative merit, but previous work on argumentation describes situations in which such arguments can be effective, such as the use of emotive arguments to draw attention away from the facts, or to frame a discussion in a particular way BIBREF18 , BIBREF19 . Furthermore, work on persuasion suggest that feeling based arguments can be more persuasive in particular circumstances, such as when the hearer shares a basis for social identity with the source (speaker) BIBREF20 , BIBREF21 , BIBREF22 , BIBREF23 , BIBREF24 . However none of this work has documented the linguistic patterns that characterize the differences in these argument types, which is a necessary first step to their automatic recognition or classification. Thus the goal of this paper is to use computational methods for pattern-learning on conversational arguments to catalog linguistic expressions and stylistic properties that distinguish Factual from Emotional arguments in these on-line debate forums. Section SECREF2 describes the manual annotations for factual and feeling in the IAC corpus. Section SECREF5 then describes how we generate lexico-syntactic patterns that occur in both types of argument styles. We use a weakly supervised pattern learner in a bootstrapping framework to automatically generate lexico-syntactic patterns from both annotated and unannotated debate posts. Section SECREF3 evaluates the precision and recall of the factual and feeling patterns learned from the annotated texts and after bootstrapping on the unannotated texts. We also present results for a supervised learner with bag-of-word features to assess the difficulty of this task. Finally, Section SECREF4 presents analyses of the linguistic expressions found by the pattern learner and presents several observations about the different types of linguistic structures found in factual and feeling based argument styles. Section SECREF5 discusses related research, and Section SECREF6 sums up and proposes possible avenues for future work.