Deep Learning Models for Sentiment Analysis in Customer Reviews
Abstract
Sentiment analysis, a subfield of natural language processing (NLP), plays a pivotal role in understanding and extracting opinions, emotions, and attitudes expressed in customer reviews. Deep learning models have emerged as powerful tools for sentiment analysis due to their ability to automatically learn intricate patterns and representations from large volumes of text data. This paper provides an overview of recent advancements in deep learning-based approaches for sentiment analysis in customer reviews. It discusses various deep learning architectures, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory networks (LSTMs), and transformer-based models, along with their applications in sentiment analysis tasks. Furthermore, the paper explores challenges and considerations related to training deep learning models for sentiment analysis, including data preprocessing, feature extraction, model selection, and evaluation metrics. Through a review of recent literature and empirical findings, this paper aims to provide insights into the state-of-the-art techniques, trends, and future directions in leveraging deep learning for sentiment analysis in customer reviews.