NEWS ARTICLES CLASSIFICATION
Mr. Pankaj yadav
Datta Megha College of Engineering
Airoli, Navi Mumbai
Mumbai, Maharashtra
Shila Jawale
Datta Megha College of Engineering
Airoli, Navi Mumbai
shilaph@gmail.com
Mr. Ashutosh Mahadik
Datta Megha College of Engineering
Airoli, Navi Mumbai
Mumbai, Maharashtra
Ms. Neha Nivalkar
Datta Megha College of Engineering
Airoli, Navi Mumbai
Mumbai, Maharashtra
Dr. S. D. Sawarkar
Datta Megha College of Engineering
Airoli, Navi Mumbai
Sudhir_sawarkar@yahoo.com
Abstract— Social media for news consumption is a double-edged sword. On the one hand, its low cost, easy access, and rapid dissemination of information lead people to seek out and consume news from social media. For the last few years, text mining has been gaining significant importance. Since Knowledge is now available to users through variety of sources e.g. electronic media, digital media, print media, and many more. Due to becoming a very hot research area, a lot of unstructured data has been recorded by research experts and have found numerous ways in literature to convert this scattered text into defined structured volume, commonly known as text classification.
Focuses on full text classification e.g. full news, huge documents, long length texts etc. is more prominent as compared to the short length text. We have discussed text classification process, classifiers, and numerous feature extraction methodologies but all in context of texts e.g. news classification based on their headlines. Existing classifiers and their working methodologies are being compared and results are presented effectively.
We also discuss related research areas, open problems, and future research directions for news article classification.
Keywords – News articles, Social Media, Unstructured Data, News Class, News Classification Algorithm.
I. INTRODUCTION
With the rapid growth of online information, text categorization has become one of the key techniques for handling and organizing text data. Text categorization techniques are used to classify news stories, to find interesting information on the World Wide Web and to guide a user’s search through hypertext. In these days, most of the available contents are in digital form. To manage such data is big challenge. The textual revolution has seen a tremendous change in the availability of online information. Finding information for just about any need has never been more automatic. Therefore, Text Classification is the task in which sorting is done automatically to classify the documents into predefined classes. Manual text classification is an expensive and time-consuming method, as it become difficult to classify millions of documents manually. Therefore, automatic text classifier is constructed using labeled documents and its accuracy is much better than manual text classification and it is less time consuming too. The proposed work includes the use of Naïve Bayes for online news classification. In the proposed work four types of news has been classified like business, sports, entertainment, political and health. Text classification is the process of assigning text documents to one or more predefined categories. This allows users to find desired information faster by searching only the relevant categories and not the entire information space. To automate the classification process, machine learning methods have been introduced. In a text classification method based on machine learning, classifiers are built (trained)with a set of training documents. The trained classifiers can therefore assign documents to their suitable categories. Online news articles represent a type of web information that are frequently referenced. It will be useful to gather news from these sources and classify them accordingly for ease reference. News Articles classification system, that performs automated news classification. Multinomial Naive Bayes classification method to classify news articles into categories. These categories can be either a set of predefined categories, i.e., general categories, or special categories defined by users themselves. The latter are also known as the personalized categories. With personalized categories, it allows users to quickly locate the desired news articles with minimum effort.
II. PROBLEM DOMAIN
Data mining is the process of sorting through large data sets to identify patterns and establish relationships to solve problems through data analysis. Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal to extract information (with intelligent methods) from a data set and transform the information into a comprehensible structure for further use. The data mining process breaks down into five steps. First, organizations collect data and load it into their data warehouses. Next, they store and manage the data, either on in-house servers or the cloud. Data mining programs analyze relationships and patterns in data based on what users request. Data mining techniques are used in many research areas, including mathematics, cybernetics, genetics and marketing. While data mining techniques are a means to drive efficiencies and predict customer behavior, if used correctly, a business can set itself apart from its competition through the use of predictive analysis. Machine learning (ML) is the study of computer algorithms that improve automatically through experience It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop conventional algorithms to perform the needed tasks. Machine learning is closely related to computational statistics, which focuses on making predictions using computers. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a related field of study, focusing on exploratory data analysis through unsupervised learning. In its application across business problems, machine learning is also referred to as predictive analytics.
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