Sentiment Analysis on Real-Time Twitter Data Using LSTM with Mutually Inclusive Classifiers

A novel approach to perform sentiment analysis on real-time Twitter data using long short-term memory (LSTM) neural networks with mutually inclusive classifiers. The work leverages the vast amount of publicly available Twitter data, enabling us to analyze the sentiments expressed by users in real-time. The Twitter data by cleaning, tokenizing, and removing stop words to create a high-quality dataset is being preprocessed. The preprocessed data is then fed into the LSTM neural network, which is known for its ability to model long-term dependencies in sequential data. To enhance the sentiment analysis performance, a mutually inclusive classifier framework is proposed. Traditional sentiment analysis models often categorize sentiments into distinct classes, such as positive and negative. In contrast, our mutually inclusive classifiers allow for multiple sentiments to be assigned to a single tweet. This approach is better suited to capture the nuances and complexities of human emotions often expressed on social media platforms like Twitter. The LSTM model with mutually inclusive classifiers using Python and popular deep learning libraries like TensorFlow and Keras is implemented. The model is trained on a labeled dataset, and hyperparameters are tuned to optimize its performance. To evaluate the effectiveness of the approach it uses various metrics, such as accuracy, precision, recall, and F1-score. In the experimental analysis, demonstrated the LSTM-based sentiment analysis model with mutually inclusive classifiers outperforms traditional sentiment analysis models. The findings contribute to the advancement of sentiment analysis techniques and offer valuable applications in understanding and analyzing user sentiments in the dynamic world of social media.