Classification of the Stance in Online Debates Using the Dependency Relations Feature

Online discussion forums offer Internet users a medium for discussions about current political debates. The debate is a system of claims regarding interactivity and representation. Users make claims in an online discussion with superior content to support their position. Factual accuracy and emotional appeal are critical attributes used to convince readers. A key challenge in debate forums is to identify the participants’ stance, each of which is inter-dependent and inter-connected. This research work aims to construct a classifier that takes the linguistic features of the posts as input and outputs predictions for the stance label of each post. Three types of features which include Lexical, Dependency, and Morphology are used to detect the stance of the posts. Lexical features such as cue words are employed as surface features, and deep features include dependency and morphology features. Multinomial Naïve Bayes classifier is used to build a model for classifying stance and the Chi-Square method is used to select the good feature set. The performance of the stance classification system is evaluated in terms of accuracy. The result of stance labels for this proposed research represents as for and against by analyzing the surface and deep features that capture the content of a post

Europe in the shadow of financial crisis: Policy Making via Stance Classification

Since 2009, the European Union (EU) is phasing a multi–year financial crisis affecting the stability of its involved countries. Our goal is to gain useful insights on the societal impact of such a strong political issue through the exploitation of topic modeling and stance classification techniques. \ \ To perform this, we unravel public’s stance towards this event and empower citizens’ participation in the decision making process, taking policy’s life cycle as a baseline. The paper introduces and evaluates a bilingual stance classification architecture, enabling a deeper understanding of how citizens’ sentiment polarity changes based on the critical political decisions taken among European countries. \ \ Through three novel empirical studies, we aim to explore and answer whether stance classification can be used to: i) determine citizens’ sentiment polarity for a series of political events by observing the diversity of opinion among European citizens, ii) predict political decisions outcome made by citizens such as a referendum call, ii) examine whether citizens’ sentiments agree with governmental decisions during each stage of a policy life cycle.

Twitter Stance Detection with Textual, Sentiment, and Target-specific Models

Today more and more users express their opinions and stances on social media platforms such as Twitter. In this paper, I proposed different approaches to automatically detect the stance of a single tweet. I investigated whether including additional sentiment polarity information and the target information would be beneficial for the stance detection task. Moreover, I also researched whether target-specific features could be generalized to other datasets with different targets for the stance detection task.Master of Science in Information Scienc

Writing History in a Supreme Court Ruling: Evaluative language in the majority opinion concerning Dobbs vs. Jackson

This paper conducts an exploratory investigation into the use of evaluative language in the historical section of the majority opinion in Dobbs v. Jackson Women’s Health Organization, 597 U.S. (2022). The investigation employs Martin & White’s (2005) Appraisal Theory, adapted specifically for the analysis of the particular evaluative features of historical discourse as elaborated on, for example, by Myskow (2018a) and Oteíza & Pinuer (2013). The findings confirm that a revised version of the Appraisal framework can be fruitfully applied to systematically account for the complex interplay between, on the one hand, the various sources of evaluation, and, on the other hand, the specific attitudinal resources, employed by the authorial voice in an attempt to construe and advance a particular view of the past. This particular ideological view is ultimately leveraged to produce a convincing justificatory argument for the overruling of the two previous landmark Supreme Court decisions that had, respectively, granted and confirmed abortion as a constitutional right in the United States of America

Directional adposition use in English, Swedish and Finnish

Directional adpositions such as to the left of describe where a Figure is in relation to a Ground. English and Swedish directional adpositions refer to the location of a Figure in relation to a Ground, whether both are static or in motion. In contrast, the Finnish directional adpositions edellä (in front of) and jäljessä (behind) solely describe the location of a moving Figure in relation to a moving Ground (Nikanne, 2003). When using directional adpositions, a frame of reference must be assumed for interpreting the meaning of directional adpositions. For example, the meaning of to the left of in English can be based on a relative (speaker or listener based) reference frame or an intrinsic (object based) reference frame (Levinson, 1996). When a Figure and a Ground are both in motion, it is possible for a Figure to be described as being behind or in front of the Ground, even if neither have intrinsic features. As shown by Walker (in preparation), there are good reasons to assume that in the latter case a motion based reference frame is involved. This means that if Finnish speakers would use edellä (in front of) and jäljessä (behind) more frequently in situations where both the Figure and Ground are in motion, a difference in reference frame use between Finnish on one hand and English and Swedish on the other could be expected. We asked native English, Swedish and Finnish speakers’ to select adpositions from a language specific list to describe the location of a Figure relative to a Ground when both were shown to be moving on a computer screen. We were interested in any differences between Finnish, English and Swedish speakers. All languages showed a predominant use of directional spatial adpositions referring to the lexical concepts TO THE LEFT OF, TO THE RIGHT OF, ABOVE and BELOW. There were no differences between the languages in directional adpositions use or reference frame use, including reference frame use based on motion. We conclude that despite differences in the grammars of the languages involved, and potential differences in reference frame system use, the three languages investigated encode Figure location in relation to Ground location in a similar way when both are in motion. Levinson, S. C. (1996). Frames of reference and Molyneux’s question: Crosslingiuistic evidence. In P. Bloom, M.A. Peterson, L. Nadel & M.F. Garrett (Eds.) Language and Space (pp.109-170). Massachusetts: MIT Press. Nikanne, U. (2003). How Finnish postpositions see the axis system. In E. van der Zee & J. Slack (Eds.), Representing direction in language and space. Oxford, UK: Oxford University Press. Walker, C. (in preparation). Motion encoding in language, the use of spatial locatives in a motion context. Unpublished doctoral dissertation, University of Lincoln, Lincoln. United Kingdo

Learning Development and Education for Sustainability: what are the links?

Learning Development (LD) is an emerging discipline developing a unique disciplinary identity. In common with many other new fields, it considers its position and relevance to other disciplines and bodies of thought, and in particular, educational development, applied linguistics and the sociology and philosophy of education. This paper considers one such area of debate: the link between Learning Development and Education for Sustainability (EfS). EfS is an area of pedagogic practice and a field of enquiry of considerable and growing importance in Higher Education (HE) and universities. Its underpinning systemic and epistemic philosophies suggest the need for integration across all facets of university activity, including LD. In this paper, we argue that there are identifiable links between LD and EfS that extend these philosophies, practices and fields of enquiry, characterised by the following: 1) commonalities surrounding the foci of their pedagogic practices, 2) shared methodologies for undertaking their practices, and 3) ways in which these methodologies are helping to situate both professions and disciplines within organisational contexts. The commonalities and possible distinctions between LD and EfS form a starting point for discussion, and raise the possibility that explicit identification of the links may encourage increased collaboration between the respective communities of practice, and the development of new ideas and innovative practice

Discourse-level Relations For Opinion Analysis

Opinion analysis deals with subjective phenomena such as judgments, evaluations, feelings, emotions, beliefs and stances. The availability of public opinion over the Internet and face to face conversations; coupled with the need to understand and mine these for end applications has motivated a great amount of research in this field in recent times. Researchers have explored a wide array of knowledge resources for opinion analysis, from words and phrases to syntactic dependencies and semantic relations.In this thesis, we investigate a discourse-level treatment for opinion analysis.In order to realize the discourse-level analysis, we propose a new linguistic representational scheme designed to support interdependent interpretations of opinions in the discourse. We adapt and extend an existing subjectivity annotation scheme to capture discourse-level relations in multi-party meeting corpus. Human inter-annotator agreement studies show that trained human annotators can recognize the elements of our linguistic scheme. Empirically, we test the impact of our discourse-level relations on fine-grained polarity classification. In this process, we also explore two different global inference models for incorporating discourse-based information to augment word-based information. Our results show that the discourse-level relations can augment and improve upon word-based methods for effective fine-grained opinion polarity classification. Further, in this thesis, we explore linguistically motivated features and a global inference paradigm for learning the discourse-level relations form the annotated data. We employ the ideas from our linguistic scheme for recognizing stances in dual-sided debates from the product and political domains. For product debates, we use web mining and rules to learn and employ elements of our discourse-level relations in an unsupervised fashion. For political debates, on the other hand, we take a more exploratory, supervised approach, and encode the building blocks of our discourse-level relations as features for stance classification. Our results show that, the ideas behind the discourse level relations can be learnt and employed effectively to improve overall stance recognition in product debates