# Towards Sentiment Analysis for Romanian Twitter Content
## 3 Data Processing Methodology
### Dataset  
For our research, we selected the Twitter US Airline Sentiment Tweets ([[https://www.kaggle.com/crowdflower/twitter-airline-sentiment](https://www.kaggle.com/crowdflower/twitter-airline-sentiment)]([https://www.kaggle.com/crowdflower/twitter-airline-sentiment](https://www.kaggle.com/crowdflower/twitter-airline-sentiment)), accessed on 1 September 2022) dataset. The data were collected in 2015 and each tweet was manually labeled by external contributors with its global polarity (positive, negative and neutral). It contains around 15,000 tweets, 63% being negative, 21% neutral and 16% positive. Each tweet is accompanied by the contributor's confidence about the annotated sentiment and each negative tweet is accompanied by a reason for the assessment. There are a number of reasons why we consider this dataset suitable for our research purpose: (i) it contains microblogging-specific (bad) language, (ii) the sentiment class of each tweet was manually annotated and we can easily verify the correctness and the reason for the annotation, (iii) the number of tweets is large enough to provide reasonable training data, and (iv) the tweets are relatively recent.  
As a first processing step, we used Google Translate ([[https://translate.google.com/](https://translate.google.com/)]([https://translate.google.com/](https://translate.google.com/)), accessed on 2 September 2022) service to translate all the tweets of the dataset in Romanian. We eliminated all duplicated rows and sorted the dataset by Tweet Id. Thus, we obtained two datasets: the original Twitter US Airline Sentiment Tweets (in English) and its Romanian translation.  
The structural, grammatical and syntactical integrity of any text translated with automated processes is affected. The main metric used in the literature to measure the quality of an automated translator is the BLEU (Bilingual Evaluation Understudy) score. This score is computed by comparing a translation with one or more acceptable translations and checking for the presence/absence of particular words, the word ordering and the degree of distortion. If the score measures from 0 to 100, a higher number represents a better translation (100 denoting perfect translation). In [58], general English texts are translated to 50 different languages using Google Translate and the BLEU score is computed. The mean score over all the compared translations is \(\approx\)76. English to Romanian achieved a score of 84, which is considerably above average. The maximum BLEU score of 91 was achieved by English to Portuguese while the minimum of 55 was achieved by English to Hindi. Similar results are also obtained in [59] where English to Romanian obtained better than average results. In both works, better translations are obtained for languages which are in the same of similar family with English. Translations from English to distant languages, such as Hindi or Hebrew, are the most negatively effected.  
For the purpose of running ML tasks, the dataset was split in training and test sets. The training dataset consists of approx. 11,000 instances, while the testing dataset consists of the remaining approx. 3700 instances, thus ensuring a 75-25% split between train and test data. The split was made just after text preprocessing, such that the class distribution between the train and test set was similar. Moreover, the English and Romanian train and test set are identical in the sense that they contain the same instances