# A Twitter-Based Comparative Analysis of Emotions and
## 2 Related Work  
Few studies have been undertaken on the cross-national emotional and sentimental characteristics of Twitter tweets. Fewer still have been carried out on developing nations [8]. Cross-cultural emotional differences regarding urban greenspace (UGS), as disclosed in English and German tweets, were investigated. A sentiment analysis was conducted on the collected tweets to determine the sentiment values and their corresponding tweet numbers. The results indicate that different emotions are elicited by different types of UGS, and that English and German demands were distinct, as evidenced by their tweets, with the highest sentiment values in gardens and parks, respectively. The activity environment contributed most to positive emotions, regardless of cultural differences. The findings of the study indicate that human emotions can indicate whether UGS supply satisfies human needs and that particular contextual factors can promote positive human emotions to sustain their needs in a cross-cultural context [9]. A novel sentiment-analysis technique that enables a comparison of the emotive contents of Twitter messages in the United States and Japan was used by researchers investigating how affective cultural values may influence social media use. The study revealed that Japanese users primarily produced low (vs. high)-arousal postings, whilst U.S. users mostly produced positive (vs. negative) posts, in line with their respective cultural and emotional values. However, in contrast to their affective cultural values, the Japanese users were more affected by changes in others' high-arousal positive(including feelings such as excitement) posts than the U.S. users than by changes in their high-arousal negative (including feelings such as fury) posts. When accounting for variations in baseline exposure to emotive content across various themes, these trends persisted. The authors claimed that, across cultures, social media users are affected by culturally relevant content that contradicts their affective values [10]. An empirical study on how Twitter users employ emojis was presented. The research used a comprehensive, cross-regional data set from Twitter to perform the analysis. The authors employed distributional semantic models to express emoji semantics and contrasted country-specific emoji models. It was observed that the categories and frequencies of the emojis expressed by users could serve as rich sources of data for understanding cultural differences between Twitter users from a wide variety of demographics. The study indicated that the preferred usage of emojis conforms to [PERSON]'s cultural dimensions model, in which different cultural dimensions within countries demonstrate considerably diverse uses of emojis to express emotions. From another perspective [11], researchers investigated how people's use of emotions on Twitter is influenced by cross-cultural variations. By combining Twitter-emoticon-usage patterns with [PERSON]'s culture index, the authors found that people from collectivistic cultures favor vertical and eye-oriented emoticons, whereas people from individualistic cultures prefer horizontal and mouth-oriented emoticons [12]. People's emotions, captured from Twitter tweets, about the Russia-Ukraine war (RUW) were examined to present a framework for automatically categorizing various societal emotions on Twitter. The study proposed a framework for automatically categorizing the many societal emotions on Twitter using a pertained ML technique, EmoRoberta. The model extracted 27 distinct emotions exhibited by Twitter users, which were then classified using machine-learning techniques. The study found that 81% of the Twitter users who participated in the survey had a neutral opinion of the RUW; however, there were hints concerning countries other than Russia and Ukraine, including Slovakia and the United States. The majority of the tweets described the RUW with terminology more closely associated with Ukraine than with Russia [13].