# Fusion Text Representations to Enhance Contextual Meaning in Sentiment Classification
## 2 Related Work
### Sentiment Classification  
In the rapidly developing digital era, information is not only widespread in large quantities but also in various forms, including text, images, and videos. One of the main challenges in managing this information is understanding the sentiment or feelings contained in a piece of text, especially to determine the author's attitude toward a particular topic, whether positive, negative, or neutral. Sentiment classification, also known as opinion mining [14], is a process that uses NLP techniques, text analysis, and computational linguistics to identify and extract subjective information from text sources. Its main goal is to determine the author's attitude toward a particular subject, whether it is a product, service, organization, individual, or other topics. Sentiment classification can be applied in various applications, one of which is product reviews [15]. Business use sentiment classification to understand customer perceptions of their products, enabling them to make improvements and develop products further. In social media analysis, it can help companies and individuals understand public opinion about a brand, service, or specific issue and can be used to monitor and respond to customer feedback effectively to enhance customer satisfaction and loyalty. Additionally, sentiment analysis can be used in the political field [16] to gauge public opinion on candidates, policies, or specific political issues to aid in political campaigns and communication strategies.  
With the various benefits that can be applied by analyzing sentiment, there are also challenges that arise. Sentiment classification works using language, which often has ambiguities such as irony and sarcasm that are difficult to detect manually. Conducting sentiment classification also heavily depends on the quality of the data and the training process. Non-representative or biased data can result in inaccurate models. To implement a sentiment classification model, several approaches can be taken. The first approach is lexicon-based [17], which uses a list of words that have been labeled with sentiments (positive, negative, or neutral). Each word in the text is compared with this list to determine the overall sentiment. The second approach is machine learning, which involves training a machine learning model on a labeled dataset to classify new text. Commonly used algorithms include Naive Bayes, Support Vector Machine (SVM), and neural network-based approaches such as Recurrent Neural Network (RNN) and Transformer [18].  
RNN is a type of deep learning model widely used to solve problems in sequence models such as text. Nowadays, deep learning is highly favored by researchers to address text-related problems, including text classification and sentiment classification. Unlike traditional methods that use classical approaches in text processing, such as bag-of-words or TF-IDF, which require manual feature extraction, deep learning employs deep neural networks capable of automatically learning to represent features from data. This means the model can capture complex patterns and contextual relationships in text without the need for manual features. This is why researchers focus their studies on using deep learning. Deep learning models commonly used for text classification include Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).