ERTIM@MC2: Diversified Argumentative Tweets Retrieval
This work addresses the task of opinion mining for festivals in social media, but it is incremental as it applies existing techniques to a specific CLEF competition dataset.
The paper tackled the problem of retrieving diverse and argumentative tweets about festivals from a multilingual collection, using a method that measured argumentation compounds and clustered tweets for diversity, achieving results described in submitted runs.
In this paper, we present our participation to CLEF MC2 2018 edition for the task 2 Mining opinion argumentation. It consists in detecting the most argumentative and diverse Tweets about some festivals in English and French from a massive multilingual collection. We measure argumentativity of a Tweet computing the amount of argumentation compounds it contains. We consider argumentation compounds as a combination between opinion expression and its support with facts and a particular structuration. Regarding diversity, we consider the amount of festival aspects covered by Tweets. An initial step filters the original dataset to fit the language and topic requirements of the task. Then, we compute and integrate linguistic descriptors to detect claims and their respective justifications in Tweets. The final step extracts the most diverse arguments by clustering Tweets according to their textual content and selecting the most argumentative ones from each cluster. We conclude the paper describing the different ways we combined the descriptors among the different runs we submitted and discussing their results.