An Efficient Sentiment Analysis Model for Crime Articles’ Comments using a Fine-tuned BERT Deep Architecture and Pre-Processing Techniques

The prevalence of social media these days allows users to exchange views on a multitude of events. Public comments on the talk-of-the-country crimes can be analyzed to understand how the overall mass sentiment changes over time. In this paper, a specialized dataset has been developed and utilized, comprising public comments from various types of online platforms, about contemporary crime events. The comments are later manually annotated with one of the three polarity values- positive, negative, and neutral. Before feeding the model with the data, some pre-processing tasks are applied to eliminate the dispensable parts each comment contains. In this study, A deep Bidirectional Encoder Representation from Transformers (BERT) is utilized for sentiment analysis from the pre-processed crime data. In order the evaluate the performance that the model exhibits, F1 score, ROC curve, and Heatmap are used. Experimental results demonstrate that the model shows F1 Score of 89% for the tested dataset. In addition, the proposed model outperforms the other state-of-the-art machine learning and deep learning models by exhibiting higher accuracy with less trainable parameters. As the model requires less trainable parameters, and hence the complexity is lower compared to other models, it is expected that the proposed model may be a suitable option for utilization in portable IoT devices.