# Towards Sentiment Analysis for Romanian Twitter Content
## 1 Introduction
[11] demonstrates that it is possible to obtain or reconstruct personal user data by single-social or cross-social network analysis. In our work, we aim to infer global sentiment polarity for social media texts and we want to clarify that our analysis is not aimed at violating or exploiting user privacy. Specifically targeting Twitter, performing SA on non-English tweets is seen as challenging, mostly because of the difficulty to gather enough labeled data in the target language [12]. Annotated datasets could be found for popular languages in the world. For example, for English, we can mention BERTweet [10], a large-scale language model trained over a corpus of 850M Tweets, which could be used together with fairseq [13] or transformers [14] for text categorization tasks, including SA. In France, DEFT challenges run between 2014 to 2018 focused on opinion mining and SA from Twitter posts [15], and they gathered a labeled dataset at the disposal of the participating teams. In Spain, TASS workshop ([[http://tass.sepln.org/](http://tass.sepln.org/)]([http://tass.sepln.org/](http://tass.sepln.org/)), accessed on 22 August 2022) held starting with 2012 at SEPLN congress supplies a dataset with annotated tweets [16], including Spanish crosslingual variations. However, little could be found for less popular languages of the world, such as Romanian. Romanian belongs to the Romance language group, having many substantial differences from English, including the alphabet, grammar, phonology, etc. In general, the same sentiment is expressed in a more verbose way in Romanian than in English. [PERSON] and [PERSON] [17] performed emotion detection over a dataset of about 4000 tweets in Romanian. The texts were manually labeled by them, but the dataset was not made publicly available. [PERSON] and [PERSON] [18] collected and manually labeled a dataset with Romanian tweets about brands and created a SA model for usage in brand monitoringand analysis. Unfortunately, their manually labeled dataset is not publicly available. To our knowledge, the recently proposed LaRoSeDa dataset [19] is the first and only public dataset dedicated to sentiment analysis in Romanian. The requirements imposed on our project [20] include a three-class sentiment prediction capability (negative, neutral and positive) and the ability to analyze social media-specific texts. These requirements make LaRoSeDa an unsuitable candidate for our work because the sentiment is labeled in a binary fashion (negative and positive) and the texts refer to product reviews collected from various online shopping platforms, not from social media platforms. A team at technobium.com (accessed on 26 August 2022) created a commercial engine for SA for the Romanian language, with a free demo posted at semitetric.ro (accessed on 26 August 2022), capable of determining polarity for various texts, including microblogging [21]. However, there are few scientific details on the internal construction of the model and its performance in both prediction capabilities and efficiency with regards to the usage of computational resources. Other efforts worth mentioning in the area of SA for Romanian are [22; 23; 24; 25; 26]. To overcome the missing linguistic resources in the underrepresented languages, the works in [27; 28; 29] suggest that automated translation could be used for model learning with respect to SA, maintaining a similar performance with the original dataset. In this research, we restrict our scope to perform SA on Twitter data (as an example of a social network with harsh limit over the size of the messages), on a under-represented language (Romanian) which lacks available labeled data to be used within model training phases. Our final goal is to produce a model for inferring the global polarity of a tweet in a multinomial classification fashion (positive, negative or neutral) with an acceptable performance, even without being in possession of a large Romanian training dataset that can meet our needs.