# Towards Sentiment Analysis for Romanian Twitter Content
## 1 Introduction  
Nowadays, social media platforms such as Twitter, Facebook or Instagram have become a common place for people to share their thoughts and for media outlets to spread their information to the world. Supervising social media becomes effective as a way of surveillance, as the number of their users has rocketed around the world: by the end of 2021, the number of social media users was over 4.2 billion, and this number is projected to increase to 5.4 billion by 2025 [1]. A mean toward this is analyzing people's opinion, sentiments, and attitudes from text sources, also known as sentiment analysis (SA) [2]. Falling under the broad umbrella of text classification, SA systematically identifies, extracts, quantifies and studies affective states and subjective information, producing useful knowledge from text, to be used in subsequent descriptive or causal analysis [3]. Due to the high popularity of social media, platforms such as Twitter have become a good source for investigating public opinions. By mining such texts, private entities can employ methods such as brand monitoring in order to capture important brand events in real time, and reflect a brand's financial value to the firm [4]. Public entities, such as governments, can use sentiment analysis or emotion detection in order to observehow the population is reacting to various public issues such as politics or healthcare. For example, [PERSON] et al. [5] analyzed the attitude of Indian citizens towards COVID-19 vaccines by collecting public social media posts written in English. They concluded that, although positive sentiments were more prevalent than negative ones, the Indian government needs to focus especially on addressing the fear of vaccines, which is a major factor contributing to the negative attitude towards vaccination. Even more knowledge can be extracted by modeling discussions on social networks. [PERSON] et al. [6] proposed a general multilayer network approach and proved its validity by applying it on a Twitter dataset containing texts concerning opinions on COVID-19 vaccines (pro-vaccination, neutral and anti-vaccination). They discovered that anti-vaxxers tend to have ego networks denser and more cohesive than those of pro-vaxxers, which leads to a larger number of interactions among anti-vaxxers. SA represents an established category of natural language processing (NLP), with a lot of research efforts being focused on discovering the machine learning (ML) methods that produce the best models given a particular problem under study. Although textbooks such as [2] or reviews such as [3; 7; 8] present in details the recommended steps to be adopted specifically for SA or with respect to a given technology applied to NLP in general and SA in particular, a lot of research space is still open in the area of SA, if certain problem-specific conditions occur, such as those induced within microblogging platforms, or handling user input from mobile devices, etc. Microblogging platforms such as Twitter, Instagram, Facebook, etc., inspire provocative questions as they feature linguistic challenges usually not seen in literary texts. [PERSON] [9] describes them as being _bad language_ and it includes emoticons, phrasal abbreviations such as _lol_, _smth_ or _lkr_, expressive lengthening of words, e.g., _coooll_, shortened words, or simply words written in a non-standard format, including those with typos, selected from some irregular vocabulary or following an informal grammar. Various reasons not in the scope of our research cause the presence of bad language in microblogging and it dramatically influences the performance of a standard NLP model applied here [10]. Another issue regarding social media networks is related to privacy threats. The goal of any social network is to enable users to safely share information, but in most cases, the users are not familiar with privacy preservation issues. The work of [PERSON] et al.