# Fusion Text Representations to Enhance Contextual Meaning in Sentiment Classification
## 2 Related Work
### Sentiment Classification Based on Deep Learning  
With the advent of deep learning, there has been a paradigm shift towards more sophisticated models that automatically learn features from raw data. The introduction of deep learning has significantly enhanced the performance of sentiment classification tasks. Convolutional Neural Networks (CNNs) have been employed to capture local dependencies and patterns in text through convolutional layers. [PERSON] et al. [19] demonstrated that simple CNN model with static vectors could achieve competitive results on sentiment classification tasks by leveraging word embeddings like word2 vec and GloVe. CNN tends to rely on convolutional features that capture local features from the text. This means that CNN will be very effective in capturing short or local patterns in the text, but it will be less effective in capturing global context or long-term relationships between words that are far apart in the text sequence. Recurrent Neural Networks (RNNs), especially variants such as Long Short-Term Memory (LSTM) or Gated Recurrent Unit (GRU), are designed to capture sequence dependencies and long-term context by processing text inputs sequentially and retaining information from previous steps. LSTM-based models have proven effective in improving sentiment classification tasks due to their ability to model sequential data and capture long-range dependencies in text. [PERSON] et al. (2020) proposed an attention-based LSTM model utilizing Word2 Vec embeddings, which significantly enhanced sentiment classification by focusing on relevant parts of the text. When it comes to sentiment classification, this is inherently tied to language processing. Currently, many researchers are studying sentiment classification across various languages, including Chinese. In a study by [PERSON] et al. (2021) they proposed a model for analyzing sentiment in product reviews in China using both lexicon and deep learning, termed SLCABG. While deep learning typically does not require human assistance to extract features, their proposed model incorporates sentiment lexicons to refine features from reviews. For the deep learning models, CNN and GRU are utilized to extract main sentiment features and their contexts from reviews. Additionally, an attention mechanism is employed to assign weights. SLCABG was evaluated using data from e-commerce platform dangdang.com, specifically focusing on book reviews. Another domain proposing sentiment classification models is movie reviews. In a study conducted by [PERSON] et al. (2022), a hybrid model combining the strengths of LSTM and CNN, termed Hybrid CNN-LSTM, was proposed. CNN efficiently extracts features using convolutional layers and max-pooling layers, while LSTM captures long-term dependencies among word sequences. The proposed model uses Word2 Vec to convert words into vectors that can be processed by the machine. Given the model's goal of analyzing movie reviews, the data used to evaluate the proposed model were from IMDB and Amazon movie reviews. The proposed model performed well compared to traditional models. Sentiment classification on movie review data was also conducted by [PERSON] et al. (2023). Unlike the previous model, their proposed model uses multi-channel CNN and bidirectional LSTM. To address the problem of extracting semantic information with CNN and local information with LSTM, an attention model was added. The model was evaluated using the IMDB dataset. By leveraging the advantages of word embedding and deep learning models, [PERSON] et al. (2024) proposed a hybrid model by combining several word embedding models and several deep learning models. Their model extracts features from a different deep learning model, then combines those features and classifies them against the corresponding sentiment analyzed using Twitter datasets in Turkish.