Deep learning

Geology

Hyperparameter optimization

Support vector machine

Oceanography

Machine learning

Baseline (sea)

Artificial intelligence

Computer science

Artificial neural network

Sentiment analysis

Convolutional neural network

Hyperparameter

The Journal of Supercomputing

As the number of users getting acquainted with the Internet is escalating rapidly, there is more user-generated content on the web. Comprehending hidden opinions, sentiments, and emotions in emails, tweets, reviews, and comments is a challenge and equally crucial for social media monitoring, brand monitoring, customer services, and market research. Sentiment analysis determines the emotional tone behind a series of words may essentially be used to understand the attitude, opinions, and emotions of users. We propose a novel long short-term memory (LSTM)–convolutional neural networks (CNN)–grid search-based deep neural network model for sentiment analysis. The study considers baseline algorithms like convolutional neural networks, K-nearest neighbor, LSTM, neural networks, LSTM–CNN, and CNN–LSTM which have been evaluated using accuracy, precision, sensitivity, specificity, and F-1 score, on multiple datasets. Our results show that the proposed model based on hyperparameter optimization outperforms other baseline models with an overall accuracy greater than 96%.

A novel LSTM–CNN–grid search-based deep neural network for sentiment analysis