# A Twitter-Based Comparative Analysis of Emotions and
## 6 Conclusions  
In this study, we examined how Twitter users communicate their sentiments and emotions in their tweets, as well as how they have adapted to the cultural qualities of their communities, as mentioned in the literature. There are certain disparities between Arabs and Hispanics, according to the data. These findings are consistent with the IDV index of [PERSON]'s cultural dimensions model and the research of several other psychologists. It is also partially consistent with [PERSON]'s emphasis on the neutral vs. emotive cultural dimension. According to the findings of this study, users exhibit emotions, feelings, and cultural traits that, in some respects, mirror those of their cultural peers. Although this study offers some fascinating contributions, it does feature some limitations. * First, not all nations can benefit equally from [PERSON]'s cultural model. For the sake of his research, [PERSON] regarded all of the Arab countries as a single entity, and, he only included a portion of the Arab countries in his model. [PERSON] also considered Spanish-speaking countries to be distinct countries rather than a region. To work around this, we chose Argentina as their representative as the competition was between Argentina and Saudi Arabia in the first place. * We wish another competition between Arabs and Hispanics had taken place in the 2022 World Cup to increase the persuasiveness and representativeness of our research. However, Saudi Arabia and Argentina were the only Arab and Hispanic teams to compete against each other during the 2022 Qatar World Cup, with the exception of the match between an Arab nation, Morocco, and Spain (a Spanish-speaking country). This match, however, was not included in our research because our purpose was to compare two developing-world populations. Spain is classified as Hispanic, but not as belonging to Latin America, meaning that it does not belong to the category of emerging nations. * The disparities between the emotionally classified tweets may indicate that machine translation from Spanish to English--both of which use Latin characters--is more effective than translation from Arabic to English, which uses a different character set. Hence, the results of the research were interpreted with caution. * We sometimes could not find a score for the entire Arab world in the literature. In such cases, we deemed Saudi Arabia to be representative of the Arab world, as the competition was between Saudi Arabia and Argentina in the first place. * Although this study adds to the body of international cultural research on the usage of new media, its sample size was modest. We only included a portion of the users in the two groups in the data we collected because the data were associated with a specific event. Each group only consisted of football fans. * We used English-based NLP and ML algorithms to extract sentiments and emotions after tweets from non-English languages were translated into English, which may have resulted in the loss of sentimental and emotional context. This may mean that the emotional analysis was not as reliable as it would have been if the tweets had originally been written in English. This research has numerous significant implications and contributions, despite its limitations:  
* The authors believe that this study is the first cross-cultural comparison of developing regions using Twitter. * The data from Twitter can be used to examine cross-cultural differences without requiring significant time or effort to define the geographic location or background culture by using the language in tweets as a cue to define a group culture. We show that the assessment of the sentiments and emotions in tweets in non-English languages after they are translated into English using our method may enable an evaluation of the degree of success of machine translation. The study also demonstrates that it is possible to recognize and classify users from various cultural origins using tweets as a basis.