<![CDATA[The Existing sentiment classification approaches that rely on single-feature representations often fail to capture the complexity of social media text, leading to suboptimal classification performance. This limitation is particularly evident in product-specific opinion mining, such as Tesla-related discussions on X (formerly Twitter), where textual expressions are highly diverse and context-dependent. Therefore, this study aims to develop a hybrid feature engineering approach that integrates multiple feature representations to improve sentiment classification performance. A dataset of 12,184 tweets was collected and automatically labeled into positive, neutral, and negative classes using a lexicon-based annotation method. The data were preprocessed through case folding, cleaning, tokenization, stopword removal, and stemming. Three types of features were extracted, including TF-IDF for statistical representation, N-gram for contextual information, and lexicon-based sentiment scores for semantic polarity. These features were combined into a unified hybrid feature vector and classified using a Support Vector Machine (SVM) model. The experimental results demonstrate that the proposed hybrid approach outperforms individual feature models. The hybrid model achieved an accuracy of 88.96% and an F1-score of 88.02%, with improvements of 6.82%, 5.69%, and 10.33% compared to TF-IDF, N-gram, and lexicon-only approaches, respectively. Additionally, the RBF kernel consistently outperformed the linear kernel, indicating the presence of non-linear patterns in sentiment data. These findings highlight the effectiveness of combining statistical, contextual, and semantic features in enhancing sentiment classification performance. This study contributes a practical and scalable hybrid feature framework for product-level sentiment analysis, which can be extended to other domains such as electric vehicles and consumer technology.]]>
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