Relevance Feature Discovery for Text Mining
Relevance feature discovery for text mining is a big challenge to guarantee the quality of discovered relevance features in text documents for describing user preferences because of large scale terms and data patterns. Most existing popular text mining and classification methods have adopted term based approaches. However, they have all suffered from the problems of polysemy and synonymy. Over the years, there has been often held the hypothesis that pattern based methods should perform better than term based ones in describing user preferences; yet, how to effectively use large scale patterns remains a hard problem in text mining. To make a breakthrough in this challenging issue, this paper presents an innovative model for relevance feature discovery. It discovers both positive and negative patterns in text documents as higher level features and deploys them over low level features (terms) It also classifies terms into categories and updates term weights based on their specificity and their distributions in patterns. Substantial experiments using this model on RCV1, TREC topics and Reuters 21578 show that the proposed model significantly outperforms both the state of the art term based methods and the pattern based methods. Keywords Text mining, text feature extraction, text classification 1.INTRODUCTION The objective of relevance feature discovery (RFD) is to find the useful features available in text documents, including both relevant and irrelevant ISRJournals and Publications Page 803 International Journal of Advanced Research in Computer Science Engineering and Information Technology Volume: 4, Issue: 3,Special Issue: 2 ,Apr,2016 ,ISSN_NO: 2321 3337 ones, for describing text mining results. This problem is also of central interest in many Web personalized applications, and has received attention from researchers in Data Mining, Machine Learning, Information Retrieval and Web Intelligence communities There are two challenging issues in using pattern mining techniques for finding relevance features in both relevant and irrelevant documents The first is the low support problem. Given a topic, long patterns are usually more specific for the topic, but they usually appear in documents with low support or frequency. If the minimum support is decreased, a lot of noisy patterns can be discovered. The second issue is the misinterpretation problem, which means the measures (e.g. "support" and "confidence" used in pattern mining turn out to be not suitable in using patterns for solving problems. For example, a highly frequent pattern (normally a short pattern)may be a general pattern since it can be frequently used in both relevant and irrelevant documents. Hence, the difficult problem is how to use discovered patterns to accurately weight useful features. There are several existing methods for solving the two challenging issues in text mining. Pattern taxonomy mining (PTM) models have been proposed in which, mining closed sequential patterns in text paragraphs and deploying them over a term space to weight useful features. Concept based model (CBM) has also been proposed to discover concepts by using natural language processing (NLP) techniques. It proposed verbargument structures to find concepts in sentences. These pattern (or concepts) based approaches have shown an important improvement in the effectiveness. However, fewer significant improvements are made compared with the best term based method because how to effectively integrate patterns in both relevant and irrelevant documents is still an open problem. Over the years, people have developed many mature term based techniques for ranking documents, information filtering and text classification. ISRJournals and Publications Page 804 International Journal of Advanced Research in Computer Science Engineering and Information Technology Volume: 4, Issue: 3,Special Issue: 2 ,Apr,2016 ,ISSN_NO: 2321 3337 Recently, several hybrid approaches were proposed for text classification. To learn term features within only relevant documents and unlabelled documents,used two term based models. In the first stage, it utilized a Rocchio classifier to extract a set of reliable irrelevant documents from the unlabeled set. In the second stage, it built a SVM classifier to classify text documents. A two stage model was also proposed which proved that the integration of the rough analysis (a term based model) and pattern taxonomy mining is the best way to design a two stage model for information filtering systems. 2. PATTERN TAXONOMY MINING Pattern taxonomy mining (a term based model) and is the best way to design a two stage model for information filtering systems. Text mining also referred to as text data mining, roughly equivalent to text analytics, refers to the process of deriving high quality information from text. High quality information is typically derived through the devising of patterns and trends through means such as statistical pattern learning. 2.1. FEATURE SELECTION Feature selection is a technique that selects a subset of features from data for modeling systems. Over the years, a variety of feature selection methods (e.g. Filter, Wrapper, Embedded and Hybrid approaches, and unsupervised or semi supervised methods) have been proposed in various fields .Feature selection is also one of important steps for text classification and information filtering which is the task of assigning documents to predefined classes. 2.2.TEXT ANALYTICS SOFTWARE Text analytics software can help by transposing words and phrases in unstructured data into numerical values which can then be linked with structured data in a database. With an iterative approach, an organization can successfully use text analytics to gain insight into content specific values such as sentiment, ISRJournals and Publications Page 805 International Journal of Advanced Research in Computer Science Engineering and Information Technology Volume: 4, Issue: 3,Special Issue: 2 ,Apr,2016 ,ISSN_NO: 2321 3337 emotion, intensity and relevance. Because text analytics technology is still considered to be an emerging technology, however, results and depth of analysis can vary wildly from vendor to vendor. ANALYSING THE PERFORMANCE OF STUDENTS AND MARK