We investigate the application of Neural Quantum Embedding and Quantum Neural Networks for sentiment analysis using the Stanford Sentiment Treebank dataset. We adapt the Neural Quantum Embedding framework, originally worked with image classification, to textual data by employing quantum embedding techniques that maximize trace distance for better data separability. Additionally, we incorporate advanced quantum feature maps and preprocessing techniques from recent quantum machine learning studies to enhance classification performance. Our experimental results demonstrate that the quantum models catch up with the classical baselines in sentiment classification accuracy, particularly in noisy intermediate-scale quantum settings. This work highlights the feasibility and potential of quantum-assisted sentiment analysis.