# PIMA: Parameter-Shared Intelligent Media Analytics Framework for Low Resource Languages
## 2 Related Works  
While a number of works have been presented that deal with each aspect of our proposed framework individually in the Greek language, no work has tackled all of them simultaneously. As such, we describe and group works that individually deal with sentiment, emotion, irony, and hate speech in Greek. Sentiment analysis of Greek texts has been a prominent research subject for over a decade [11]. However, most progress occurred during the early research years of the subject and since then has seemed to stagnate, with most approaches investigating a combination of sentiment resources and machine learning models such as naive Bayes classifiers and support vector machines [12; 13; 14; 15; 16]. Specifically, the use of an English and Greek sentiment and emotion lexicon has been investigated in [12], while a more targeted approach to social media using entity recognition-guided sentiment analysis, along with lexicon, is presented in [17]. Twitter specific features along with lexicons are also investigated in different contexts in [13; 16], while the importance of sentiment analysis in hotel reviews is discussed in [18]. Similarly, the focus of [19] is on identifying election trends via twitter data through sentiment analysis. Ref [14] investigated sentiment analysis on different levels of document granularity, starting from the document level and reaching the entity level, while [15] investigated the use of lexicons towards the creation of hybrid embeddings. The most recent works focus on the usability of sentiment analysis towards identifying trends in social media with respect to the pandemic, rather than creating better sentiment analysis models [20; 21]. One exception is [22], which aims to create a dataset for sentiment classification of the Homeric Texts. This study identifies difficulties in reviewer agreement between different classes, which is largely based on the artistic side of the texts and the difference in perceiving contexts by the annotators. The extensive use of lexicon has also driven research to finding better approaches towards creation of Greek specific lexicons for the task of sentiment analysis and the creation of a publicly available lexicon [23]. A recent survey on methods proposed for Greek sentiment analysis identifies the importance of word representations for Greek sentiment analysis, highlighting the performance gains acquired from different language models and the limited number of available training data [11], findings also supported by an investigative work of Greek product reviews [24]. This argument is also made stronger by the finding that language has shifted over the years [25], which suggests that datasets created in different periods to the embedding resources would not be a suitable input source. It quickly becomes apparent, both through the findings of the previously discussed works and the methodologies used, that Greek language resources are scarce. This difference in approaches becomes more prominent when comparing methodologies for Greek sentiment analysis to those used on English documents and other non low-resource languages [26]. What is more, all works have considered only a single source of data (e.g., Twitter) and cater to the specific features present in them. In this work, we aim to solve all of these issues by utilizing a significantly larger manually annotated dataset from diverse media sources (Section 3.2). Furthermore, we use an even larger corpus from the same sources to solve issues of language evolution and align the embeddings with our modern data (Section 3.1). Emotion classification of Greek texts is a significantly under-researched area compared to sentiment analysis, mostly due to the lack of available resources. However, due to the tight relation of the two tasks, emotion classification approaches are on par with modern Greek sentiment analysis approaches. Specifically, transformer-based approaches have been evaluated in [22; 27], analyzing the effectiveness of emotion classification with different source language models.