Big Data-Driven Deep Learning for Natural Language Processing

Sentiment analysis, a crucial task in natural language processing (NLP), aims to extract and classify sentiments expressed in textual data. This research delves into the application of deep learning techniques, powered by Big Data, to enhance sentiment analysis accuracy. By leveraging a substantial Amazon review dataset, we train a simple feedforward neural network to classify sentiments as positive or negative. The model employs embedding layers to represent words as dense vectors, followed by a global average pooling layer to capture semantic information. A final dense layer with a sigmoid activation function predicts the sentiment probability. The results demonstrate the effectiveness of deep learning in capturing complex linguistic nuances and achieving high accuracy. With an accuracy of 88.47%, the model outperforms traditional methods, showcasing the potential of Big Data and deep learning in sentiment analysis. Future research directions include exploring more sophisticated architectures, addressing class imbalance issues, improving model interpretability, and incorporating domain-specific knowledge to further enhance sentiment analysis performance.