PIMA: Parameter-Shared Intelligent Media Analytics Framework for Low Resource Languages

Featured Application Our work aims to provide a media analytics framework for the Greek language that utilizes subjectivity similarities among the related classification tasks, with potential for application to other low-resource languages. Media analysis (MA) is an evolving area of research in the field of text mining and an important research area for intelligent media analytics. The fundamental purpose of MA is to obtain valuable insights that help to improve many different areas of business, and ultimately customer experience, through the computational treatment of opinions, sentiments, and subjectivity on mostly highly subjective text types. These texts can come from social media, the internet, and news articles with clearly defined and unique targets. Additionally, MA-related fields include emotion, irony, and hate speech detection, which are usually tackled independently from one another without leveraging the contextual similarity between them, mainly attributed to the lack of annotated datasets. In this paper, we present a unified framework to the complete intelligent media analysis, where we propose a shared parameter layer architecture with a joint learning approach that takes advantage of each separate task for the classification of sentiments, emotions, irony, and hate speech in texts. The proposed approach was evaluated on Greek expert-annotated texts from social media posts, news articles, and internet articles such as blog posts and opinion pieces. The results show that this joint classification approach improves the classification effectiveness of each task in terms of the micro-averaged F1-score.

Automated summary

The paper introduces a media analytics framework for the Greek language, which utilizes subjectivity similarities among related classification tasks and has the potential for application to other low-resource languages. Media analysis is crucial for obtaining valuable insights from subjective text types, such as social media posts and news articles, to improve various areas of business and customer experience. The proposed unified framework incorporates sentiment, emotion, irony, and hate speech detection, enhancing the classification effectiveness for each task.