###### Abstract
Many accurate pre-trained models are already available for resource-rich languages (see Table 1, including for the sentiment analysis tasks); therefore, machine translation of less-resourced languages into rich-resourced has also been investigated. Improvements in statistical or neural machine translation systems eliminate the need to create separate monolingual Sentiment analysis models for separate languages [23; 24; 25; 26; 27; 28; 29; 30; 31; 32]. For example, in [33], neural machine translation is used to convert the multilingual data set into English which is later classified as the English language model. If the source and target languages, computational expenses can be minimized by applying word-to-word translation [34]. Nevertheless, the importance of machine translation cannot be overestimated: the machine translation accuracy of non-normative language texts is not always reliable and can introduce even more noise. We present the following contributions to the research field. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Benefit** & **Solution** & **Complexity** \\ \hline BERT & Pre-trained model in more than 100 & Question answering, Abstract & Learns contextual \\ BERT & languages and it can be tuned by adding one output layer & summarization & relation between words in a sentence/text \\ \hline XLM-R & Trained in around & Cross-lingual & It does not require lang tensor to understand \\  & 100 different languages & transfer tasks & which language is used, and should be able to \\  & & & & determine the correct \\ ELMo & Improves functions across vast & Answer questions, Textual entailment, & \
\begin{tabular}{c} Pre-trained on a huge text-corpus and learned \\ functions from deep \\ bi-directional models (biLM) \\ \end{tabular} \\ XLNet & Unlike BERT it does not need to & Sentiment Analysis, Question &
\begin{tabular}{c} Large bidirectional \\ transformer with \\ improved training \\ in terms of large amount data and \\ more computational power to achieve better than \\ BERT prediction \\ \end{tabular} \\ \hline Zero-Shot classification & No training data needed & Text Classification & It classifies objects to a different label that the \\  & classifier has not been trained on. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of pre-trained NLP models. * The zero-shot model detects emotions first, and later they are used to assign positive, negative, and neutral sentiments. Such a method gradually decreases the dimensionality starting from the high-dimensional sentence transformer input (i.e., vectorized text) mapped into probability values of different emotions; probability values are further mapped into the sentiment labels. * The second-stage input does not require complicated feature extraction or sophisticated machine learning methods able to catch sentiments directly from the text, which, in turn, speeds up the whole sentiment analysis process. * The performance of the proposed method is evaluated on three benchmark datasets (IMDB, Sentiment140, and SemEval-2017) and using multiple classifiers, including machine learning, neural network, and ensemble learning. * The proposed emotion-sentiment detection model requires fewer training data compared to traditional Sentiment analysis detection. This paper is divided into five more sections. In Section 2, we present the related work of existing solutions. The methods used in this experiment are described in Section 3. In Section 4, we present our experiment results and discuss the results obtained in Section 5. Section 6 summarizes, concludes our work, and provides our thoughts on possible future research directions.