ENHANCING CRIME DATA ANALYSIS THROUGH WORD-VECTOR CONVERSION: A RESNIK-GLOVE APPROACH

Crime data analysis is being performed now according to the requirement of Natural Language Processing (NLP) and machine learning algorithms that are able to extract insights from vast amounts of unstructured text data. There have been several efforts to apply classical word embedding models such as Glove, BERT, Skip-Gram, and Bag of Words, but they never identify the explicit meaning of crime tapes. This paper presents a novel hybrid method in which Resnik's similarity measure is combined with the GloVe algorithm to improve the semantic representation of text crime data. Our method employs the Resnik-GloVe model to generate word vectors for crime descriptions and addresses that both capture global word co-occurrence as well as semantic similarity. An Entropy swish based convolutional dense neural network (ES-CDNN) is learned to enhance classifier accuracy by incorporating them. The experiments performed with the 2016 San Francisco crime dataset validate that the Resnik-GloVe method actually generates an outstanding result by obtaining 98.33% accuracy whereas GloVe achieved 96.25%, BERT 94.26%, Skip-Gram 92.15%, and Bag of Words reached as low as 90.66%. The suggested methodology utilizes improved classification of crime data and helps law enforcement officers and policymakers analyze crime patterns more effectively. The research contributes to the area of crime analytics by addressing important shortcomings in existing word embedding methods and demonstrating the real benefits of integrating semantic similarity measures with conventional NLP models.