# Analysing Social Media Discourse on Electric Vehicles with Machine Learning
## 6 Conclusions  
In this study, sentiment analysis and text mining techniques were used on Turkish and English tweet data to understand the social acceptance of EVs, consumers' electric vehicle purchasing behaviours, and consumer perception and acceptance processes about EVs. The data collected through the social media platform X reflect the social perception of EVs and play an important role in determining the factors that shape this perception. The findings show that EVs are generally welcomed positively, but there are also some concerns. For instance, sentiment analysis revealed a higher share of neutral tweets in Turkish data and more negative tweets in English data, reflecting differing stages of public awareness and infrastructure readiness. Topic modelling identified key concerns, including charging infrastructure, battery life, cost, and brand perception, all of which shape social acceptance. The analysis of the general sentiment about EVs revealed that positive comments were more common in both Turkish and English tweets. However, more neutral sentiments were found in Turkish tweets, suggesting that users have a neutral and objective attitude towards learning more about EVs. In English-language tweets, negative comments are more common, indicating that concerns and challenges about EVs are being expressed more globally. This result suggests that some global factors affecting the acceptance of EVs, especially economic and technical barriers, constitute obstacles to their widespread adoption in society. The findings on the social acceptance of EVs show that environmental benefits are emphasised in a positive way. Users appreciate the environmentally friendly features of EVs, stating that they reduce dependence on fossil fuels and cause less damage to the environment. However, some important concerns limiting the acceptance of EVs have also come to the fore. Technical and economic concerns such as battery life, charging infrastructure and vehicle prices were more frequently mentioned, especially in English tweets. This reflects users' concerns about the high economic costs and limited range of EVs. Furthermore, the lack of widespread availability of charging stations and long charging times for batteries are also among the factors hindering the wider acceptance of these vehicles. Although Turkish and English tweets expressed similar concerns about these issues, cultural differences were also observed. While Turkish tweets are characterised by more neutral language and neutral comments, English tweets show more negative emotionsand concerns. This reveals the differences in the approach of both societies towards EVs and the cultural factors affecting the acceptance of these vehicles. This study presents important findings on the social acceptance of EVs by comparing Turkish and English social media data. Although there are similar studies in the literature, this study offers a new perspective on how social perception is shaped by language and culture by comparing social media data in different languages. In addition, environmental concerns and technical barriers, which have frequently been emphasised in previous studies on the social acceptance of EVs, are discussed in more detail in this study, and the differences between Turkish and English tweets are revealed. This contributes to a better understanding of public perceptions of EVs. The findings obtained in this study are of particular importance in terms of understanding the factors affecting the consumer decision-making process for EVs. These findings significantly overlap with previous research in the literature. The results of sentiment analysis and topic modelling on social media show that consumers' perceptions of EVs are significantly affected by factors such as economic advantages, environmental concerns, range anxiety, and charging infrastructure. The findings of the study are in line with the results of [PERSON] et al. (2019), who reported that range anxiety and inadequate charging infrastructure negatively affect consumer decisions [72].