With the dramatic increase in the amount of content available in digital forms gives rise to a problem to manage this online textual data. As a result, it has become a necessary to classify large texts (documents) into specific classes. And Text Classification is a text mining technique which is used to classify the text documents into predefined classes. Most text classification techniques wor...

In text classification, one key problem is its inherent dichotomy of polysemy and synonym; the other problem is the insufficient usage of abundant useful, but unlabeled text documents. Targeting on solving these problems, we incorporate a sprinkling Latent Semantic Indexing (LSI) with background knowledge for text classification. The motivation comes from: 1) LSI is a popular technique for info...

Indonesian government has developed a system for citizens to voice their aspirations and complaints, which are then stored in the form of short documents. Unfortunately, the existing system employs human annotators to manually categorize the short documents, which is very expensive and time-consuming. As a result, automatically classifying the short documents into their correct topics will redu...

On the internet huge data are in the uncategorized form. Big information is hidden behind this uncategorized scene of data. If classification of these internet documents done, then it will be helpful in many cases. All the documents related to a single class can be found at the single location. This paper considers the different text categorization systems. These systems are using different cla...

Traditional supervised learning makes the closed-world assumption that the classes appeared in the test data must have appeared in training. This also applies to text learning or text classification. As learning is used increasingly in dynamic open environments where some new/test documents may not belong to any of the training classes, identifying these novel documents during classification pr...

We investigate the following problem: Given a set of documents of a particular topic or class P , and a large set M of mixed documents that contains documents from class P and other types of documents, identify the documents from class P in M . The key feature of this problem is that there is no labeled nonP document, which makes traditional machine learning techniques inapplicable, as they all...

The enormous amount of information stored in unstructured texts cannot simply be used for further processing by computers, which typically handle text as simple sequences of character strings. Text mining is the process of extracting interesting information and knowledge from unstructured text. One key difficulty with text classification learning algorithms is that they require many hand-labele...

Text classification and feature selection plays an important role for correctly identifying the documents into particular category, due to the explosive growth of the textual information from the electronic digital documents as well as world wide web. In the text mining present challenge is to select important or relevant feature from large and vast amount of features in the data set. The aim o...