# A Twitter-Based Comparative Analysis of Emotions and
###### Abstract  
Twitter is one of the best online platforms for social interaction, introducing unique means of story-telling through tweets and enabling multiple approaches to the analysis of their content. This study was motivated by the increasing practice of incorporating Twitter into cultural studies and the research gap in Twitter-based cultural studies between emerging nations. This research aims to examine the emotional and sentimental cultural traits of Arabic and Hispanic viewers of a specific football match, as shown through their tweets, regardless of their distinct languages, to determine whether cultural diversity can be noticed in online interaction. Hundreds of tweets from both communities were translated into English as an intermediate language and then evaluated and contrasted using machine learning (ML) models. According to the research, Arabs are more collectivistic (as opposed to individualistic) and, as a result, exhibit less emotional arousal than Hispanics, which was partially supported by the collected Twitter data. This demonstrates how Twitter could play a key part in cultural research, and, therefore, this study contributes to cross-national comparative cultural research. We demonstrate that our method can also be used to evaluate the quality of machine translation based on how effectively it captures the emotions and sentiments of original languages. [table]capposition=top  
determine whether there are significant cross-national differences in how emotional and sentimental tweets are expressed and, if so, how these differences relate to the cultural differences noted by researchers. Although several academics have examined this topic, few have provided cross-national comparisons [4]. We can therefore anticipate fewer cross-national comparisons between developing world regions and nations due to the absence of financial support for technological and cultural advancement. Additionally, this may be due to a variety of linguistic factors [5]. In a group of languages, including Arabic, Turkish, and Hebron, each input word can be composed of multiple lexical and functional elements, making natural language processing (NLP) extremely difficult [6]. Due to issues such as the grammatical complexity of the Arabic language, its variety of dialects, and the need for reliable data sources, there is a lack of Arabic-language research in NLP, particularly in summarization [7]. Amharic is among the languages with the fewest resources in ML. Therefore, it requires cost-effective strategies and enormous quantities of annotated training data, as well as techniques distinct from those used for the English language. Consequently, in developing nations, we can expect a small number of successfully developed NLP algorithms, which are frequently built on the foundation of machine learning (ML) algorithms. This paper utilizes a mixed-method approach, which we believe to be the most suitable technique. First, all the tweets published in Arabic and Spanish during the Argentina vs. Saudi Arabia World Cup 2022 football match were gathered. Next, they were analyzed quantitatively in terms of the number of tweets and their distribution between Arabs and Hispanics. The qualitative-content-analysis technique was then used to probe into the texts to uncover cultural differences hidden within users' emotional and semantic traits. The proposed model in this method comprises successive phases in which the outcome of each phase builds upon the previous phase's result. Therefore, we collected the tweets during the specified duration and separated them into Arabic and Spanish groups. Next, we translated both texts into English as an intermediary language and performed an emotional and sentimental analysis of the translated material. The final step was to classify the gathered emotions as low or high in arousal, based on the distribution of basic emotions in the arousal-valence space.