# PIMA: Parameter-Shared Intelligent Media Analytics Framework for Low Resource Languages
## 1 Introduction  
Modern day-to-day life is tightly coupled with interacting with various sources of information. Such sources span from news outlets such as CNN ([[https://edition.cnn.com](https://edition.cnn.com)]([https://edition.cnn.com](https://edition.cnn.com)) (accessed on 28 February 2023)) and the BBC ([[https://www.bbc.com](https://www.bbc.com)]([https://www.bbc.com](https://www.bbc.com)) (accessed on 28 February 2023)), to social media sites such as Facebook ([[https://www.facebook.com](https://www.facebook.com)]([https://www.facebook.com](https://www.facebook.com)) (accessed on 28 February 2023)) and Twitter ([[https://twitter.com](https://twitter.com)]([https://twitter.com](https://twitter.com)) (accessed on 28 February 2023)), or even online retailers such as Amazon ([[https://www.amazon.com](https://www.amazon.com)]([https://www.amazon.com](https://www.amazon.com)) (accessed on 28 February 2023)) and eBay ([[https://www.ebay.com](https://www.ebay.com)]([https://www.ebay.com](https://www.ebay.com)) (accessed on 28 February 2023)). Media analytics is tasked with utilizing the ever-growing content presented in these public forums in an intelligent manner in order to produce valuable insights for its end users [1]. Commonly, insights such as the sentiment and the emotion expressed in such forums is important information and has been the subject of extensive research [2, 3]. Additionally, depending on the type of the content's source, information such as the existence of irony and hate speech are also highly sought after in the field of social media analytics [4, 5, 6, 7]. Subjective expressions, such as those that describe an individual's feelings towards a particular topic, brand, or product, hold great value for industries that aim to gather, analyze, and present this information in a meaningful way. Such insights can be used todrive business directions and product revisions, and in the case of hate speech and irony detection, make platforms a safer, less toxic place [8; 9]. Hence, a lot of effort is being put into developing state-of-the-art approaches for this type of content analysis. Intuitively, it becomes clear that while all these target outcomes (i.e., emotion, sentiment, irony, and hate speech) are very different in their desired intent, they are also very tightly coupled in the way that they are expressed. However, most approaches treat them as individual goals and do not investigate the existence of underlying connections, so that one target can help define the others, and vice versa, in a uniform process and create better performing intelligent systems. This can be partially attributed to the lack of annotated parallel data, which could facilitate the investigation and exploitation of such parallel interconnections within text, leading to sequential approaches following a continuous learning paradigm. Moreover, while this type of analysis is driven by intelligent systems, they lack the support of some, if not all, of these target outcomes for many languages other than English. These languages, characterized as low-resource due to the lack of fundamental resources, such as available training data and language specific tools, lag behind their English counterpart, as they are unable to develop intelligent tools and they face existing language specific challenges [10]. In this paper, we propose an intelligent media analytics framework for the Greek language, trained to identify sentiments, emotions, irony, and hate speech simultaneously. To facilitate this research, we also describe a newly collected and manually annotated dataset with parallel annotations for all target outcomes from diverse sources. We further investigate the connection between these different subjective expressions with respect to the performance in correctly classifying content for each target outcome through a series of experimental settings and ablations studies. Finally, we present a state-of-the-art model for Greek media analytics, which demonstrates a significant performance improvement over the individual models.