English Semantic Similarity based on Map Reduce Classification for Agricultural Complaints

Due to environmental changes, including global warming, climatic changes, ecological impact, and dangerous diseases like the Coronavirus epidemic. Since coronavirus is a hazardous disease that causes many deaths, government of Egypt undertook many strict regulations, including lockdowns and social distancing measures. These circumstances have affected agricultural experts' presence to help farmers or advise on solving agricultural problems. For helping this issue, this work focused on improving support for farmers on the major field crops in Egypt Retrieving solutions corresponding to farmer query. For our work, we have mainly focused on detecting the semantic similarity between large agriculture dataset and user queries using Latent Semantic Analysis (LSA) based on Term Frequency Weighting and Inverse Document Frequency (TF-IDF) method. In this research paper, we apply SVM MapReduce classifier as a framework for paralleling and distributing the work on the dataset to classify the dataset. Then we apply different approaches for computing the similarity of sentences. We presented a system based on semantic similarity methods and support vector machine algorithm to detect the similar complaints of the user query. Finally, we run different experiments to evaluate the performance and efficiency of the proposed system as the system performs approximately 77.8%~94.8% in F-score measure. The experimental results show that the accuracy of SVM classifier is approximately 88.68%~89.63% and noted the leverage of SVM classification to the semantic similarity measure between sentences.