# Fusion Text Representations to Enhance Contextual Meaning in Sentiment Classification
###### Abstract  
Sentiment classification plays a crucial role in evaluating user feedback. Today, online media users can freely provide their reviews with few restrictions. User reviews on social media are often disorganized and challenging to classify as positive or negative comments. This task becomes even more difficult when dealing with large amounts of data, making sentiment classification necessary. Automating sentiment classification involves text classification processes, commonly performed using deep learning methods. The classification process using deep learning models is closely tied to text representation. This step is critical as it affects the quality of the data being processed by the deep learning model. Traditional text representation methods often overlook the contextual meaning of sentences, leading to potential misclassification by the model. In this study, we propose a novel fusion text representation model, GloWord_biGRU, designed to enhance the contextual understanding of sentences for sentiment classification. Firstly, we combine the advantages of GloVe and Word2 Vec to obtain richer and more meaningful word representations. GloVe provides word representations based on global frequency statistics within a large corpus, while Word2 Vec generates word vectors that capture local contextual relationships. By integrating these two approaches, we enhance the quality of word representations used in our model. During the classification stage, we employ biGRU, considering the use of fewer parameters, which consequently reduces computational requirements. We evaluate the proposed model using the IMDB dataset. Several scenarios demonstrate that our proposed model achieves superior performance, with an F1 score of 90.21%. word embedding; sentiment classification; deep learning 2024 14 10420 13390/0  
On the other hand, these reviews are also very beneficial for the service providers or businesses. They will utilize data from customer reviews to make business decisions. These business decisions can include continuing to produce a product or making decisions to improve the quality of their products and services to consumers. The process of analyzing reviews refers to analyzing the review texts based on customer emotions and obtaining trends in customer emotions. However, as the number of reviews on online platforms increases, the resulting data also grow, making it nearly impossible to manually search and analyze these reviews. This is where the role of sentiment classification comes in. Sentiment classification of a review involves classifying the text based on the tendency of emotions as positive, negative, or neutral. The initial stage of developing sentiment analysis processes was largely carried out using lexicon and machine learning models such as Naive Bayes [2], SVM [3,4], and Decision Tree [5,6]. However, these models require manual human assistance in the feature extraction process. With the advancement of research in sentiment classification, deep learning [7] has become a more promising model compared to lexicon and machine learning models. Unlike machine learning, deep learning does not require human intervention to find and form features. Deep learning automatically extracts features from different neural network models. This makes deep learning models more widely used today. A widely used variant of deep learning suitable for text due to its sequential nature is Recurrent Neural Networks (RNNs) [8], which consist of Long Short-Term Memory (LSTM) [9] and Gated Recurrent Units (GRUs) [10], as well as their bidirectional variants, namely Bidirectional Long Short-Term Memory (biLSTM) and Bidirectional Gated Recurrent Unit (biGRU). LSTM and GRU are both effective in addressing the vanishing gradient problem and capturing long-term dependencies. GRU has a simpler architecture compared to LSTM because it only uses two gates (reset gate and update gate) compared to the three gates used in LSTM (input gate, output gate, and forget gate).