News document analysis by using a proficient algorithm
Journal Title: International Journal of engineering Research and Applications - Year 2017, Vol 7, Issue 6
Abstract
News articles analyzing is one of the emerging research topic in the past few years. News paper discusses various types (political, education, employment, sports, agriculture, crime, medicine, business, etc) of news in different levels such as International, National, state and district level. In this news articles, crime discussion plays a major role because one crime leads to a many other crimes and also affect many other lives. In India, Madurai is one of the important places which have many historical monuments. Madurai is a sensitive place. This paper analyzes the crimes which occur in the year 2015 in and around Madurai. This analysis helps to police department to reduce the occurrence of crime in the future. This proposed system used Support Vector Machine (SVM) for effectively classify the document. News documents are preprocessed using pruning and stemming. From the stemmed words, the informative words are selected and weighted using feature selection methods such as Term-Frequency and Inverse Document Frequency (TF-IDF) and Chi-square. It returns the high dimensional vector space. It is reduced to low dimension using Latent Semantic Analysis (LSA) method. Compute the cosine similarity between the key document and news documents. Based on the value, the news documents are labeled as crime and non-crime. Some of the documents are used to train the SVM classifier. Some of the documents are used to test the performance of developed system. From the comparative study, it is identified that the performance of the proposed approach improves the classification accuracy.
Authors and Affiliations
K. Meena, R. Lawrance
Keywords
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- EP ID EP391413
- DOI 10.9790/9622-076020713.
- Views 123
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