Sentiment Analysis with Cnn-Lstm Model for Opinion Mining

Abstract

Sentiment analysis is an important research area in natural language processing that focuses on identifying opinions, emotions, and attitudes expressed in textual data. With the increasing popularity of social media platforms and online review systems, large volumes of user-generated content are created every day. Extracting meaningful insights from this textual data requires advanced analytical techniques. This research proposes a hybrid deep learning model that combines convolutional neural networks (CNN) and long short-term memory (LSTM) networks for sentiment classification. The component captures long-term contextual dependencies. Experimental results demonstrate that the hybrid CNN-LSTM architecture significantly improves sentiment classification accuracy compared to traditional machine learning approaches.