The evolution of argumentation mining: From models to social media and emerging tools

Abstract

Argumentation mining is a rising subject in the computational linguistics domain focusing on extracting structured arguments from natural text, often from unstructured or noisy text. The initial approaches on modeling arguments was aiming to identify a flawless argument on specific fields (Law, Scientific Papers) serving specific needs (completeness, effectiveness). With the emerge of Web 2.0 and the explosion in the use of social media both the diffusion of the data and the argument structure have changed. In this survey article, we bridge the gap between theoretical approaches of argumentation mining and pragmatic schemes that satisfy the needs of social media generated data, recognizing the need for adapting more flexible and expandable schemes, capable to adjust to the argumentation conditions that exist in social media. We review, compare, and classify existing approaches, techniques and tools, identifying the positive outcome of combining tasks and features, and eventually propose a conceptual architecture framework. The proposed theoretical framework is an argumentation mining scheme able to identify the distinct sub-tasks and capture the needs of social media text, revealing the need for adopting more flexible and extensible frameworks.

Traditional Machine Learning and Deep Learning Models for Argumentation Mining in Russian Texts

“…Argumentation (or argument) mining is a field of computational linguistics that is devoted to extracting from texts and classifying arguments and relations between them, as well as constructing an argumentation structure [16], [19]. This area is seeing an influx of research activity -for example, since 2014, seven workshops on the analysis of arguments have already been held 1 .…”

Exploiting Open Data to analyze discussion and controversy in online citizen participation

“…First, Natural Language Processing and Opinion Mining techniques may be applied to analyze the citizens comments on proposals. Among others, opinion lexicons (Hubert et al, 2018), controversy vocabularies (Mejova et al, 2014;Roitman et al, 2016), language models (Jang et al, 2016) and word embeddings (Rethmeier et al, 2018), and argument extraction methods and tools (Shum et al, 2008;Lytos et al, 2019;Dutta et al, 2019) could be used to extract statements and claims in favor and against each proposal, and hence, achieving a better understanding of the most important and urgent citizen needs, as well as the underlying discussions and controversies. Moreover, applying machine learning to automatically classify and predict the relevance and levels of discussion and controversy (Jang et al, 2016;Rad and Barbosa, 2012) of citizen proposals represents a research line whose results would be of special interest for government decision and policy making.…”

Sentiment Analysis of Roman-Urdu Tweets about Covid-19 Using Machine Learning Approach: A Systematic Literature Review

“…In the first phase data will be collected using twitter API. After the complete collection of dataset, preprocessing will be performed to clean the data because clean and labeled data gives good results with machine learning algorithms [30]. Roman-Urdu does not have any labeled dataset, so in order to conduct research; data will be collected and manually labeled by the annotator.…”