ISSN: 2277-9655 [Tilve* et al., 6(2): February, 2017] Impact Factor: 4.116 IC™ Value: 3.00 CODEN: IJESS7 http: // www.ijesrt.com © International Journal of Engineering Sciences & Research Technology [513] IJESRT INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY A SURVEY ON MACHINE LEARNING TECHNIQUES FOR TEXT CLASSIFICATION Amey K. Shet Tilve*, Surabhi N. Jain * Department of Computer Engineering Don Bosco College of Engineering Margao, India Creative Capsule InfoTech Verna, India DOI: 10.5281/zenodo.322477 ABSTRACT This research focuses on Text Classification. Text classification is the task of automatically sorting a set of documents into categories from a predefined set. The domain of this research is the combination of information retrieval (IR) technology, Data mining and machine learning (ML) technology. This research will outline the fundamental traits of the technologies involved. This research uses three text classification algorithms (Naive Bayes, VSM for text classification and the new technique -Use of Stanford Tagger for text classification) to classify documents into different categories, which is trained on two different datasets (20 Newsgroups and New news dataset for five categories).In regards to the above classification strategies, Naïve Bayes is potentially good at serving as a text classification model due to its simplicity. KEYWORDS: Text Classification, Information Retrieval, Naive Bayes Classifier, Vector Space Model Text Classification, Part of Speech Tagging, Natural Language Processing. INTRODUCTION The text mining studies are gaining more importance recently because of the availability of the increasing number of the electronic documents from a variety of sources. Which include unstructured and semi structured information. The main goal of text mining is to enable users to extract information from textual resources and deals with the operations like, retrieval, classification (supervised, unsupervised and semi supervised) and summarization Natural Language Processing (NLP), Data Mining, and Machine Learning techniques work together to automatically classify the documents and discover patterns from different types of the documents . Text classification (TC) is an important part of text mining, looked to be that of manually building automatic TC systems by means of knowledge-engineering techniques, i.e. manually defining a set of logical rules also called as training , that convert expert knowledge on how to classify documents under the given set of categories. For example would be to automatically label each incoming news with a topic like “sports”, “politics”, or “business”. A data mining classification task starts with a training set D = (d1….. dn) of documents that are already labeled with a class C1, C2 (e.g. sport, politics). The task is then to determine a classification model which is able to assign the correct class to a new document d of the domain. Basically there are two stages involved in Text Classification. Training stage and testing stage. As explained in the above paragraph, in training stage documents are pre-processed and are trained by a learning algorithm to generate the classifier. In testing stage, a validation of classifier is performed. There are many traditional learning algorithms to train the data, such as Decision trees, Naïve-Bayes (NB), Support Vector Machines (SVM), k- Nearest Neighbor (kNN), Neural Network (NNet),etc. In this research, we study the problem of text classification, that is classifying the news documents into different categories based on three different supervised algorithms namely Naive Bayes classifier, Vector Space Model for text classification and a new technique -Use of Stanford Tagger for text classification. We have tried to compare the efficiency and accuracy of the algorithms to analyze the effectiveness of each algorithm. The research has been carried out on two different datasets namely 20Newsgroup and New Dataset of news for five categories.