## 2 Related Work
The performance of four variations of Naive Bayes was thoroughly evaluated in the study conducted by [PERSON] et al. [38]. This research explored two vectorization techniques, namely TF-IDF and count vectorizer, using movie review datasets. The results highlighted that TF-IDF offered a slight improvement in classification performance compared to the count vectorizer, making it a more effective approach for text feature extraction. Deep learning-based sentiment analysis has seen significant advancements. [PERSON] and [PERSON] [39] introduced a novel self-supervised sentiment classification method that employs semantic labeling based on contextual embeddings. By leveraging cosine similarity to evaluate semantic relationships in positive and negative words, they successfully trained a hybrid CNN-GRU model for sentiment classification, achieving remarkable accuracy in handling unseen data. In the judicial domain, [PERSON] et al. [40] proposed a machine learning framework combining LSTM and CNN algorithms to analyze Canadian maritime case law. Their model included two core components: one for text classification using word embeddings and another for event detection in time-series data via RNNs. This innovative approach improved the efficiency of legal document analysis. Hybrid architectures have also gained traction in sentiment analysis. [PERSON] and [PERSON] [41] developed a model integrating BERT, BiLSTM, and CNN for assessing sentiment and satisfaction in Turkish university-related tweets. Their approach leveraged multi-layered contextual embeddings to provide accurate predictions. Similarly, [PERSON] et al. [42] proposed the Transformer- and Attention-based Bidirectional LSTM (TRABSA) model. This hybrid framework combined RoBERTa-based transformers, attention mechanisms, and BiLSTM networks to improve sentiment analysis, particularly in aspect-based tasks. A sentiment analysis method for e-commerce product reviews, named Weighted Parallel Hybrid Deep Learning (WPHDL-SAEPR), has been introduced, utilizing the word2 vec model for word embeddings and combining the Restricted Boltzmann Machine (RBM) and Singular Value Decomposition (SVD) for sentiment classification [43]. Similarly, researchers explored an ensemble model leveraging transformers and Large Language Models (LLMs) for sentiment analysis in foreign languages. This approach involved translating texts into English using LibreTranslate and Google Translate before conducting sentiment analysis, demonstrating that analyzing sentiments in English effectively captures sentiments from foreign languages [44]. In a different context, a study focused on feature extraction from social media and text data by employing methods such as Bag of Words, TF-IDF, Hashing Vectorizer, N-grams, and word embeddings. The Chi-Square feature selection technique was applied to enhance model accuracy by eliminating irrelevant features [45]. Some researchers have taken an aspect-based sentiment analysis (ABSA) perspective, distinguishing sentiments related to specific aspects of a topic, product, or service. For instance, one study transitioned from word embedding to sentence embedding, leveraging the SBERT transformer model, while also innovating in label generation and topic modeling through Bayesian search clustering combined with Inverse Document Frequency (IDF) calculations [46]. To address the scarcity of labeled data in ABSA tasks, another approach utilized LLMs such as GPT-3.5-turbo (OpenAI, San Francisco, CA, USA) and Llama-3-70B (Meta Platforms, Inc., Menlo Park, CA, USA) to generate synthetic examples, thereby supporting model training in low-resource scenarios [47]. Further advancements include leveraging BERT's contextual understanding with affine attention mechanisms to define relationships between words in ABSA tasks, coupled with the Multi-Layered Enhanced Graph Convolutional Network (MLEGCN) model to improve relational complexity and word pair compatibility [48].