CNN-RNN: A Hybrid Convolutional and Recurrent Neural Network Approach for Cross-Domain Sentiment Analysis Using the Webemo Dataset

Globally, e-commerce and social media sites are rapidly improving every day. Social media is a dynamic platform where people can share their thoughts and feelings. Sentiment analysis plays a crucial role in understanding public sentiment. The existing method has some drawbacks, including difficulties in emotion detection and a small amount of data used for analysis. However, for cross-domain sentiment analysis using the WEBEmo dataset, the proposed model combines the convolutional neural network (CNN) and recurrent neural network (RNN) methods. This dataset contains both text and emoticon data. In the pre-processing stage, sentences are divided into individual tokens, which are then converted into numerical form using word embedding techniques such as Word2Vec and the GloVe process. Finally, padding is applied to ensure that each input preparation has an identical length. Convolutional layers are added to reduce dimensionality and extract local features from pre-processed data. Finally, the Long Short Term Memory (LSTM) approach is used to capture both long-range dependencies and context information. This hybrid model’s parameters are being tuned to improve sentiment analysis accuracy. The experimental results show that the proposed hybrid CNN-RNN model outperforms state-of-the-art methods on the WEBEmo dataset, with an accuracy of 96.02%. This model also provides numerous performance analyses. In contrast to DANN, this model improves cross-domain flexibility while overcoming the limitations of individual techniques like LSTM, RNN, and CNN. It is well-defined for sentimental analysis tasks that require both local and sequential feature learning.