With the technological advancements, global communication has largely shifted to text-based communication. As a result, the process of extracting meaningful insights from human behavior by machine learning techniques applied to textual data has now been significantly simplified. This research utilizes text mining methods to analyze customers feedback from food reviews, employing them as effective tools for opinion analysis and rating prediction from feedback. This research utilizes two neural network techniques (Normal Neural Network and LSTM) to analyze textual data and generate predicted scores ranging from 1 to 5 for each review from Amazon food review dataset. After implementing two neural network models, the system automatically generates a predicted score on a scale from 1 to 5. This study employs widely-used neural network techniques and provides a foundation for advancing text-based emotion detection in future research. The primary focus of this study is on unaltered customer feedback and it aims to solve the problem of accurately analyzing customer sentiments or opinion and extracting meaningful insights from their feedback. By comparing the performance of LSTM and standard neural networks, we achieve a 62.12% accuracy, showcasing superior results in emotion prediction from unstructured textual reviews. These insights pave the way for more scalable and efficient solutions in text mining for emotion detection.