###### Abstract  
We live in a digitized era where our daily life depends on using online resources. Businesses consider the opinions of their customers, while people rely on the reviews/comments of other users before buying specific products or services. These reviews/comments are usually provided in the non-normative natural language within different contexts and domains (in social media, forums, news, blogs, etc.). Sentiment classification plays an important role in analyzing such texts collected from users by assigning positive, negative, and sometimes neutral sentiment values to each of them. Moreover, these texts typically contain many expressed or hidden emotions (such as happiness, sadness, etc.) that could contribute significantly to identifying sentiments. We address the emotion detection problem as part of the sentiment analysis task and propose a two-stage emotion detection methodology. The first stage is the unsupervised zero-shot learning model based on a sentence transformer returning the probabilities for subsets of 34 emotions (anger, sadness, disgust, fear, joy, happiness, admiration, affection, anguish, caution, confusion, desire, disappointment, attraction, envy, excitement, grief, hope, horror, joy, love, loneliness, pleasure, fear, generosity, rage, relief, satisfaction, sorrow, wonder, sympathy, shame, terror, and panic). The output of the zero-shot model is used as an input for the second stage, which trains the machine learning classifier on the sentiment labels in a supervised manner using ensemble learning. The proposed hybrid semi-supervised method achieves the highest accuracy of 87.3% on the English SemEval 2017 dataset. sentiment analysis; emotion detection; sentence transformers; zero-shot model; ensemble learning; natural language processing  
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tool able to analyze these texts automatically, helping companies/service providers to respond quickly and adequately [6]. However, it can also be misused for spreading disinformation and hate speech, which can be detected automatically using sentiment analysis methods [7; 8; 9; 10]. Moreover, sentiment analysis even allows tracking of trends in real time by monitoring the popularity of products/services and even political candidates; and forecasting voting results [11; 12]. Here we focus on automatic sentiment analysis as one of the popular NLP problems that have found many areas of applications by analyzing customers' product reviews, social media, survey unstructured responses, etc. The goal of sentiment analysis is to analyze, generalize, and predict whether the text contains subjectivity and expresses the sentiment, apart from which sentiment is the dominant [13; 14]. Most sentiment analysis studies focus on assigning positive, negative, and sometimes neutral sentiment values to the given text. A less studied direction in sentiment analysis is to move the aim from analyzing sentiment toward a specific item to the internal mood of the text itself [15]. Zero-shot cross-lingual experiments present the evaluation of monolingual models applied to another language [16; 17; 18; 19]. Most models function well with commonly used languages such as English; however, applying these algorithms straight to low-quality corpora frequently yields disappointing results [20]. Cross-lingual sentiment analysis aims to employ high-quality and rich resources in English to improve the classification performance of resource-scar languages [21]. These methods address the problem of training separate models for each language, but despite that, they lack mechanisms able to modify traditional training methods (classifiers) for the Sentiment analysis task. To our current knowledge, emotion detection and sentiment classification are two different tasks that are often solved independently [22]. However, in this paper, we assume that the sentiment analysis problem can be tackled more effectively if we rely on emotion detection first.