Sentiment Analysis of News from Security Perspective

Abstract

Illuminating Security Threats Hidden in News Sentiments. First, we analyzed sentiment analysis different approaches and methods also which models are applicable and their performance, then to get good performance in sentiment analysis comments samples and a speech comprehensive dataset were collected and used for training and evaluating the models. Three different models were trained for comment sentiment analysis, namely Naive Bayes, Support Vector Classifier (SVC), and Long Short-Term Memory (LSTM) model. For comment sentiment analysis, the models were trained on a dataset consisting of labeled comments which we generated using VADER sentiment. The accuracy, recall, precision, and F1-score were used as evaluation metrics. The results showed that the LSTM model outperformed the Naive Bayes and SVC models in Performance metrics. For speech emotion recognition, the LSTM model was trained on a dataset consisting of labeled speech samples. The same evaluation measures were used as performance metrics. The results showed that the LSTM model achieved a final good Metrics suite. The results obtained suggest that the LSTM model is a promising approach for both tasks and could be further improved with additional data and optimization. To better capture the complexity of emotions, we’re taking a multi-faceted approach in our research. We’re not relying on words alone; instead, we’re looking at how tone, pitch, and rhythm of speech, along with facial expressions and gestures, contribute to conveying feelings. By combining text, sound, and visual cues, our multi-modal sentiment analysis offers a fuller, more accurate picture of the emotions within news stories. This approach goes beyond what traditional methods can do, providing a deeper understanding of sentiments and any potential hidden risks they carry. With advanced machine learning at the core, this method is set to open new doors in understanding the nuanced world of emotional expression.