The paper presents the snake species identification by using natural language processing. It aims to help medical professionals in predicting the snake species for snake-bite treatments based on the patient’s description of the snake. The decision in suitable anti-venom critically depends on the type of snake species. Wrong anti-venom may result in severe morbidity and mortality. This research investigates the human perception and the selection of words in describing a snake based on their visual view. The descriptions were presented in unstructured text, and the NLP processing involves pre-processing, feature extraction and classification. Four machine learning algorithms (naïve Bayes, k-Nearest Neighbour, Support Vector Machine, and Decision Trees J48) were used during training and classification. Our results show that J48 algorithm obtained the highest classification accuracy of 71.6% correct prediction for the NLP-Snake data set with high precision and recall.
