Sentiment Analysis Using Hybrid Model of Stacked Auto-Encoder-Based Feature Extraction and Long Short Term Memory-Based Classification Approach

Customer reviews about a brand or product, movie reviews, and social media reviews can be analyzed through sentiment analysis. Sentiment analysis is used to identify the emotional tone of language to comprehend the attitudes, opinions, and feelings represented in online reviews. As for large data, it is a task that can take a lot of time and can be automated as the machine learns through the training and testing of data. Previously, various standard machine learning and deep learning models namely Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), Long Short Term Memory (LSTM), Naïve Bayes (NB), Support Vector Machine (SVM), Gated Recurrent Unit (GRU) have been used. The key issue in our research is that when text is provided to LSTM directly, it cannot adequately extract informative features from the text, leading to less accurate findings. The softmax layer of Stacked Auto-encoder when used directly to categorize the extracted features, is power-constrained and unable to do so accurately. A hybrid of the Stacked Auto-encoder (SAE) and LSTM models was proposed. SAE is used for the extraction of relevant informative features. LSTM was used for further classification of sentiments based on the extracted features. The proposed model is evaluated on an IMDB dataset by splitting it into five different training testing ratios using the following performance evaluation metrics: confusion matrix, classification accuracy, precision, recall, sensitivity, specificity, and F1 score. The hybrid results performed best at a ratio of 90/10 and classified sentiments with an accuracy of 87%. The accuracy of proposed hybrid model is better than that of standard models namely RNN, CNN, LSTM, NB, SVM, and GRU.