Opinion and sentiment mining of web-based content are widely done to find out the views of users about consumer goods or politics, but the techniques rely on accrual, do not identify justification, and do not provide structure to support reasoning. Argument mining provides an articulated view of web-based content, identifying justifications, counterpoints, and structure for reasoning.
Two other papers were presented at the meetup.
One by Francesca Toni and Lucas Carstens from Imperial College:
Sentiment Analysis is concerned with differentiating opinionated text from factual text and, in the case of opinionated text, determine its polarity. With this paper, we present A-SVM, a system that tackles the discrimination of opinionated text from non-opinionated text with the help of Support Vector Machines (SVM). In a two-step process, SVM classifications are improved via arguments, acquired by means of a user feedback mechanism. The system has been used to investigate the merits of approaching Sentiment Analysis in a multi faceted manner by comparing straightforward Machine Learning techniques with this multimodal system architecture. All evaluations were executed using a purpose-built corpus of annotated text and its classification performance was compared to that of SVM. The classification of a test set of approximately 12,000 words yielded an increase in classification precision of 5.6%.
Another paper by Francesca Toni and Valentinos Evripidou from Imperial College
We describe a new argumentation method for analysing opinion exchanges between on-line users aiding them to draw informative, structured and meaningful information. Our method combines different factors, such as social support drawn from votes and attacking/supporting relations between opinions interpreted as abstract arguments. We show a prototype web application which puts into use this method to offer anintelligent business directory allowing users to engage in debate and aid them to extract the dominant, emerging public opinion.
By Adam Wyner
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