CORTEXA
← Browse
crossrefApplied Sciences2025-04-16Cited by 6

Analysing Social Media Discourse on Electric Vehicles with Machine Learning

Yasin Özkara, Yasemin Bilişli, Fatih Serdar Yildirim, Fahrettin Kayan, Agah Başdeğirmen, Mehmet Kayakuş, Fatma Yiğit Açıkgöz

Social acceptance of electric vehicles is of great importance for environmental sustainability and economic development. This study aims to examine Turkish and English tweets about electric vehicles with sentiment analysis, text mining, and topic modelling techniques to reveal consumers’ electric vehicle purchasing behaviours, consumer perception and acceptance processes about electric vehicles, and social perceptions. The data was taken from the X platform, and high accuracy and F1 scores were obtained in both languages in the classification made with the deep learning-based LSTM model. The accuracy was 92.1% for English tweets and 96.7% for Turkish tweets. According to the sentiment analysis results, the perception of electric vehicles is generally positive in both languages. However, while the rate of neutral sentiment is higher in Turkish tweets, the rate of negative sentiment is higher in English tweets. This indicates that there is more criticism and debate about electric vehicles globally, while Turkish tweets have more neutral views on the subject. Word frequency analysis shows that positive comments about electric vehicles focus on economic and environmental advantages, while negative comments include concerns about charging time, battery life, and range concerns. The topic modelling identified three main themes related to electric vehicles: (1) reasons for being preferred by consumers and their purchasing tendencies, (2) the role of brands, (3) market developments and marketing strategies. In Turkish tweets, electric vehicle production, charging infrastructure, and consumer purchasing trends were at the forefront. In general, it is emphasised that charging infrastructure should be strengthened, battery performance should be improved, and costs should be reduced to accelerate the adoption of electric vehicles.

View free PDFSource page

Related papers

crossrefApplied Sciences2024-12-06Cited by 8

Charging Strategies for Electric Vehicles Using a Machine Learning Load Forecasting Approach for Residential Buildings in Canada

Ahmad Mohsenimanesh, Evgueniy Entchev

The global electric vehicle (EV) market is experiencing exponential growth, driven by technological advancements, environmental awareness, and government incentives. As EV adoption accelerates, it introduces opportunities and challenges for power systems worldwide due to the larg…

View free PDFSource page
crossrefApplied Sciences2022-11-16Cited by 9

CAVeCTIR: Matching Cyber Threat Intelligence Reports on Connected and Autonomous Vehicles Using Machine Learning

George E. Raptis, Christina Katsini, Christos Alexakos, Athanasios Kalogeras, Dimitrios Serpanos

Connected and automated vehicles (CAVs) are getting a lot of attention these days as their technology becomes more mature and they benefit from the Internet-of-Vehicles (IoV) ecosystem. CAVs attract malicious activities that jeopardize security and safety dimensions. The cybersec…

View free PDFSource page
crossrefApplied Sciences2024-03-09Cited by 49

Comparative Analysis of Commonly Used Machine Learning Approaches for Li-Ion Battery Performance Prediction and Management in Electric Vehicles

Saadin Oyucu, Ferdi Doğan, Ahmet Aksöz, Emre Biçer

The significant role of Li-ion batteries (LIBs) in electric vehicles (EVs) emphasizes their advantages in terms of energy density, being lightweight, and being environmentally sustainable. Despite their obstacles, such as costs, safety concerns, and recycling challenges, LIBs are…

View free PDFSource page
crossrefApplied Sciences2026-02-03

The Relationship Between Breakdowns and Production, and the Detection of Breakdown Units in Mining Vehicles Using Machine Learning

Erol Gödur, Yalçın Çebi, Ahmet Hakan Onur

The mining industry relies heavily on large-scale machinery, making operational efficiency highly sensitive to equipment breakdowns and maintenance interruptions. Such breakdowns directly affect production performance, operational costs, and planning accuracy. Therefore, the abil…

View free PDFSource page
crossrefApplied Sciences2024-08-26Cited by 15

Machine-Learning-Based Path Loss Prediction for Vehicle-to-Vehicle Communication in Highway Environments

Nugman Sagir, Zeynep Hasirci Tugcu

Vehicle-to-vehicle (V2V) communication, which plays an important role in intelligent transportation systems, has been statistically proven to improve traffic efficiency and reduce the probability of accidents. In real-world applications, it is critical to accurately estimate the…

View free PDFSource page