Many machine learning algorithms have been applied to text classification tasks. In the machine learning paradigm, a general inductive process automatically builds a text classifier by learning, generally known as supervised learning. However, the supervised learning approaches have some problems. The most notable problem is that they require a large number of labeled training documents for acc...

Text classification is the process of classifying documents into predefined categories based on their content. It is the automated assignment of natural language texts to predefined categories. Text classification is the primary requirement of text retrieval systems, which retrieve texts in response to a user query, and text understanding systems, which transform text in some way such as produc...

Text classification is the process of classifying documents into predefined categories based on their content. It is the automated assignment of natural language texts to predefined categories. Text classification is the primary requirement of text retrieval systems, which retrieve texts in response to a user query, and text understanding systems, which transform text in some way such as produc...

Text classification plays an large part in today's world, where the amount of information available is overwhelming. Although most text classification work is related to topical classification, the categorization of more subjective documents that depend more on style and the author's opinion is also important. Websites such as Amazon, IMDB, and Rotten Tomatoes rely on opinions and reviews to ke...

The objective of this thesis is to develop efficient text classification models to classify text documents. In usual text mining algorithms, a document is represented as a vector whose dimension is the number of distinct keywords in it, which can be very large. Consequently, traditional text classification can be computationally expensive. In this work, feature extraction through the non-negati...

Text categorization is the process of classifying documents into a predefined set of categories based on its contents of keywords. Text classification is an extended type of text categorization where the text is further categorized into sub-categories. Many algorithms have been proposed and implemented to solve the problem of English text categorization and classification. However, few studies ...

Automatic text classification is the problem of automatically assigning predefined categories to free text documents, thus allowing for less manual labors required by traditional classification methods. When we apply binary classification to multi-class classification for text classification, we usually use the one-against-the-rest method. In this method, if a document belongs to a particular c...

text mining is a technique to find meaningful patterns from the available text documents. The pattern discovery from the text and document organization of document is a well-known problem in data mining. Analysis of text content and categorization of the documents is a complex task of data mining. In order to find an efficient and effective technique for text categorization, various techniques ...

Classification of Text Document points towards associating one or more predefined categories based on the likelihood expressed by the training set of labeled documents. Many machine learning algorithms plays an important role in training the system with predefined categories. The importance of Machine learning approach has felt because of which the study has been taken up for text document clas...

Classification of text documents become a need in today’s world due to increase in the availability of electronic data over internet. Till now, no text classifier is available for the classification of Punjabi documents. The objective of the work is to find best Punjabi Text Classifier for Punjabi language. Two new algorithms, Ontology Based Classification and Hybrid Approach (which is the comb...