# Fusion Text Representations to Enhance Contextual Meaning in Sentiment Classification
## 2 Related Work
### Sentiment Classification Based on Deep Learning
The output of the character level embedding is then used as an input from CNN for feature extraction, while the output from word embedding is used as the input of various variants of the RNN model for feature extraction. The resulting two features are then combined and use the softmax function to classify sentiment. [PERSON] et al. (2025) incorporated linguistic knowledge into their proposed models by integrating POS information and dependency relationships. Their approach combines these linguistic features with statistically learned word vectors, resulting in a more comprehensive understanding of the input text. Additionally, convolutional operations were employed to merge POS tags, dependency relationships, and statistically learned word vectors. This method effectively captures the interplay between different linguistic features and statistical patterns, leading to improved performance in aspect-based sentiment analysis. [PERSON] et al. (2026) tackled the challenge of leveraging both original and multiscale high-level context features by developing a scalable multi-channel dilated CNN-BiLSTM model for sentiment analysis. This model utilizes a dilated CNN-BiLSTM architecture to capture features at different scales and employs a multi-channel approach to integrate original and high-level context features. Their method was tested on two public Chinese text datasets, showing significant improvements. [PERSON] et al. [27] addressed challenges in sentiment classification, such as complex semantics and multi-sentiment polarity, by proposing a scalable multi-channel CNN and Bidirectional GRU model with an attention mechanism to enhance feature extraction and contextual understanding for improved sentiment classification. [PERSON] et al. [28] addressed the complexity in existing sentiment classification models by proposing a simpler and more robust approach that uses a single bidirectional LSTM layer combined with global maximum and average pooling layers, focusing on efficiently capturing long-term word dependencies. [PERSON] et al. [29] aimed to solve the limitations of RNNs and CNNs in sentence modeling, specifically the restricted global semantics in RNNs and the limited performance of CNNs due to convolutional filter widths. They proposed an Attention-based Multichannel Convolutional Neural Network (AMCNN), which combines BiLSTM for contextual encoding and scalar and vectorial attention mechanisms to generate multichannel representations, effectively enhancing n-gram feature extraction and word relations. Context-independent word embeddings such as Word2 Vec [30] and GloVe [31] represent words as fixed vectors irrespective of their context in a sentence. These embeddings have been foundational in many early deep learning models for sentiment classification. The key advantage of these embeddings lies in their ability to capture syntactic and semantic similarities between words, which improves the performance of sentiment classification models. Recent studies have continued to explore the utility of these embeddings. For instance, [PERSON] et al. [32] used GloVe embeddings in conjunction with a CNN-LSTM model for sentiment classification. Their approach effectively captures both local features with CNNs and long-term dependencies with LSTMs, demonstrating significant improvements in performance on sentiment analysis benchmarks.