# Towards Sentiment Analysis for Romanian Twitter Content
## 5 Conclusions  
Within the larger setup of a media surveillance project [20], we constructed a system capable of inferring the global sentiment polarity of Romanian tweets starting from an English dataset specific to the aviation industry, translated to Romanian. This paper describes our experience in designing the classification system and extracts several noteworthy conclusions in sentiment analysis of microblogging content for Romanian. As similar works treat the Romanian SA task only as a binary classification, we set the benchmark accuracy for a multinomial methodology consisting of three classes: negative, positive, and neutral. Bernoulli NB trained on TF-IDF features achieved an accuracy of around 78%, while BERT achieved the best result of 81%. After carefully processing the Twitter data in order to properly approach bad language in particular, we built and evaluated models constructed with the help of various classifiers, including standard machine learning or the very popular nowadays deep learning. Given the large number of parameters to optimize for fine-tuning the classifiers, we opted to perform hyperparameter search with the help of evolutionary optimization. We found that the Bernoulli Naive Bayes classifier is the most robust one to both aviation industry specific tweets or to general ones and TF-IDF encoding should be used if no additional linguistic resources are available. Regarding the performance measured with the help of accuracy and weighted F1-measure, we notice that it does not differ significantly for the English original dataset and its Romanian translation. Furthermore, although the training data were specific to aviation industry, classification performance achieved on a small dataset with general tweets seems to be slightly better than one of a commercial public demo available on the market. Learning standard deep neural networks on a TF-IDF encoding or LSTM on a Word2 Vec encoding bring in comparable results, but with an increased computational cost. Doc2 Vec encoding seems not to help, as results are worse, regardless of the classifier. Further research is still needed in order to obtain even better results. LSTM requires pre-trained Word2 Vec embeddings for the target language, which are not available for Romanian. Moreover, a larger and more balanced dataset on the general domain could be used for learning, but probably with an increased computational cost. Conceptualization, D.C.N., A.B.R., M.G. and M.A.B. ; methodology, D.C.N., A.B.R., M.G. and M.A.B. ; software, D.C.N., M.G., M.A.B., N.B. and A.G.; validation, D.C.N., A.B.R., M.G., M.A.B., N.B. and A.G.; formal analysis, D.C.N. ; investigation, D.C.N., M.G. and M.A.B. ; resources, A.B.R. ; data curation, D.C.N. and M.G. ; writing--original draft preparation, D.C.N. and A.B.R. ; writing--review and editing, D.C.N., A.B.R., M.G., M.A.B., N.B. and A.G.; visualization, D.C.N. and A.B.R. ; project administration, A.B.R. ; funding acquisition, A.B.R. All authors have read and agreed to the published version of the manuscript. This paper was financed by the project with the title \"Platforma inovativa pentru masurarea audientei TV, identificarea automata a telespectorior sl corelarea cu date analitice din platforme de socializare online\" (Innovative platform for measuring TV audience, automatic identification of viewers and correlating it with analytic data from social media). The project was co-financed by \"Fondul Europen de Dezvoltare Regionala Prim Programal Operational Competitivate (POC) 2014-2020, Axa prioriards: 2-Tehnologia Informatieii S Comunicatilor (TIC) pentru o economic digitala competitiva.\" (the European Regional Development Fund (ERDF) through the Competitiveness Operational Program 2014-2020, Priority Axis 2--Information and Communication Technology (ICT) for a competitive digital economy), project code SMIS 2014+: 128960, beneficiary: CICADA TECHNOLOGIES S.R.L.