# Analysing Social Media Discourse on Electric Vehicles with Machine Learning
## 4 Results  
Table 2 shows the classification performance of Turkish and English tweets about EVs using the deep learning-based LSTM model. The success metrics of the model are evaluated in terms of precision, recall, accuracy, and F1 score, and high accuracy is achieved in both languages. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Metrics** & **English Tweet** & **Turkish Tweet** \\ \hline Precision & 0.932 & 0.972 \\ \hline Recall & 0.953 & 0.981 \\ \hline Accuracy & 0.921 & 0.967 \\ \hline F Score (F1) & 0.942 & 0.977 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance measurement. Table 2 shows the classification success of Turkish and English tweets about EVs using the deep learning-based LSTM model. Looking at the precision values, most of the positive predictions of the model were correct in both languages, with 97.2% success for Turkish tweets and 93.2% success for English tweets. Recall values are also quite high, reaching 98.1% for Turkish tweets and 95.3% for English tweets, indicating that the model can largely accurately capture the positive class. Accuracy results show an accuracy of 96.7% for Turkish tweets and 92.1% for English tweets, indicating that the overall performance of the model is successful. The F1 score balances both precision and recall performance of the model, reaching high values of 97.7% for Turkish tweets and 94.2% for English tweets. These results show that the model provides high accuracy, precision, and sensitivity in both languages and thus can effectively classify tweets about EVs. Figure 1 shows the categorisation of tweets according to sentiment. The results of the sentiment analysis of Turkish and English tweets about EVs using deep learning-based models are shown in Figure 1: 46.8% of Turkish tweets were positive, 15.7% were negative, and 37.5% were neutral. In English tweets, a 51.8% positive, 22.6% negative, and 25.6% neutral sentiment distribution was determined. This sentiment distribution shows that the general perception towards EVs is predominantly positive in both languages. The slightly higher proportion of positive tweets in English (51.8%) may suggest that there is more widespread acceptance and support for EVs globally. On the other hand, the relatively high percentage of negative posts in English tweets (22.6%) suggests that some concerns or criticisms in this area are also noteworthy. The higher proportion of neutral tweets (37.5%) in Turkish tweets may indicate that there is more neutral commentary or information sharing on the topic and that users refrain from expressing a definite positive or negative opinion. In general, although the positive perception of EVs is strong in both languages, the presence of negative sentiment rates indicates that factors such as price, lack of infrastructure, range anxiety, or battery life are still a matter of debate. The higher rate of negative sentiment, especially in English posts, suggests that there is more debate and criticism in the global market, while the relatively high rate of neutral sentiment in Turkish posts may suggest that EVs are not yet widespread enough in Turkey and that people are trying to learn more about the subject. Word frequency analysis is an important method to identify consumers' perceptions of EVs and topics of discussion [68]. Within the scope of this analysis, the most frequently used words in positive, negative, and neutral contexts were analysed, and meaningful inferences were drawn about how consumers evaluate EVs. Table 3 shows the most frequently used words in English tweets, and Table 4 shows the most frequently used words in Turkish tweets. Figure 1: Emotion analysis. The text mining analysis on EVs provides important clues in determining consumers' perceptions of these vehicles. The words \"price\", \"use\", \"use\", \"money\", \"market\", and \"green\" that are most frequently mentioned in a positive context indicate that EVs stand out for their economic advantages and environmental benefits.