# Towards Sentiment Analysis for Romanian Twitter Content
## 2 Related Work
In [52], this pretrained hybrid model was used to infer the sentiment polarity of over 800,000 Persian tweets collected over a span of 6 months. The tweets refer to an Iranian COVID-19 vaccine called COVIran Barekat and foreign vaccines (AstraZeneca, Pfizer, Moderna and Sinopharm). The authors compared the sentiments expressed towards the Iranian vaccine versus the sentiments expressed towards the foreign vaccines and found a slight preference for the Iranian one. By obtaining a monthly distribution of opinions, between April and September of 2021, the authors discovered an increase in negative sentiments towards all vaccines between late August and September. A possible explanation could be related to the reported side effects of some of the vaccines. These results seem very promising but, as stated in Section 1, we do not have the necessary resources in order to manually collect and label a large volume of Romanian texts. The state of the art in SA for microblogging content is less advanced in under-resourced languages such as Romanian. We mention here the efforts of [PERSON] and [PERSON] [17] for emotion detection and [PERSON] and [PERSON] [18] for binary sentiment analysis who report promising results, or the company technobium.com (accessed on 26 August 2022) who show a free demo posted at semitmetric.ro (accessed on 26 August 2022). Yet we could hardly rely on those tools to incorporate them in a larger project, as, to our knowledge, a Romanian microblogging content dataset similar to BERTweet is not available. As stated in Section 1, LaRoSeDa (Large Romanian Sentiment Dataset) seems to be the only publicly available Romanian dataset labeled for sentiment analysis. The dataset contains 15,000 reviews, of which 7500 are labeled positive and 7500 negative. Due to its nature all works using this resource report the performances achieved for sentiment classification in a binary fashion. In [53], an F1 score of 54% is achieved while in the work which introduces the LaRoSeDa dataset an accuracy of \(\approx\)91% is reported as the benchmark [19]. More recently, we acknowledge the Romanian DistilBERT corpus ([[https://github.com/racai-ai/Romanian-DistilBERT](https://github.com/racai-ai/Romanian-DistilBERT)]([https://github.com/racai-ai/Romanian-DistilBERT](https://github.com/racai-ai/Romanian-DistilBERT)), accessed on 30 August 2022) [54] which could be employed for binary SA over standard text data. They report the state-of-the-art binary classification accuracy of 98% for SA performed on LoReSaDa. All the previous experiments were performed on standard text in a binary classification fashion and the problem setting is different from our global polarity inference. Regarding the multinomial SA of social media texts in Romanian, we could not find any published work in order to set a benchmark with which we can compare. Searching for other Romanian research relevant to SA, we mention [PERSON] and [PERSON] [22] who developed a Romanian Emotions Lexicon, attaching tags to words. [PERSON] and [PERSON] [26] developed RoWordNet ([[https://github.com/dumitrescustefan/RoWordNet](https://github.com/dumitrescustefan/RoWordNet)]([https://github.com/dumitrescustefan/RoWordNet](https://github.com/dumitrescustefan/RoWordNet)), accessed on 30 August 2022), a semantic network of words based on the idea introduced by the WordNet English lexicon--each word having attached a polarity score. Both lexicons could be of use in sentiment analysis if a dictionary-based method (as defined in [3] p. 541) is selected for SA. Other Romanian authors performed sentiment analysis on different sort of input data than tweets, such as speech [23; 24] or poetry [25], thus benefiting from resources developed with other purposes, such as SRoL [55] which was developed to help Romanian speech processing research. Constructing language resources for under-represented languages by automatic translation seems to work well for various NLP tasks, as acknowledged in [27; 28; 29; 56].