In this paper, we propose a method to raise the accuracy of text classification based on latent topics, reconsidering the techniques necessary for good classification — for example, to decide important sentences in a document, the sentences with important words are usually regarded as important sentences.

Text classification is an essential issue in the field of natural language processing and many techniques using latent topics have so far been proposed and used under many purposes.

In this paper, we aim to raise the accuracy of text classification using latent information by reconsidering elemental techniques necessary for good classification in the following three points: 1) important words extraction

— to decide important words in documents is a crucial issue for text classification , tfidf is often used to decide them.

Many studies have proposed to improve the accuracy of text classification .

ment for text classification , there are many studies which use the PageRank algorithm.

(2005) have introduced association rule mining to decide important words for text classification .

SSL techniques are often effective in text classification , where labeled data is scarce but large unlabeled corpora are readily available.

In this paper, we show that improving marginal word frequency estimates using unlabeled data can enable semi-supervised text classification that scales to massive unlabeled data sets.

This is problematic for text classification over large unlabeled corpora like the Web: new target concepts (new tasks and new topics of interest) arise frequently, and performing even a single pass over a large corpus for each new target concept is intractable.

In this paper, we present a new SSL text classification approach that scales to large corpora.

In the text classification setting , each feature value wd represents count of observations of word 21) in document d. MNB makes the simplifying assumption that word occurrences are conditionally independent of each other given the class (+ or —) of the example.

We evaluate on two text classification tasks: topic classification, and sentiment detection.

Our experiments demonstrate that MNB-FM outperforms previous approaches across multiple text classification techniques including topic classification and sentiment analysis.

This paper addresses the problem of dealing with a collection of labeled training documents, especially annotating negative training documents and presents a method of text classification from positive and unlabeled data.

The research described in this paper involved text classification using positive and unlabeled data.

The remaining data consisting 607,259 from 20 Nov 1996 to 19 Aug 1997 is used as a test data for text classification .

3.2 Text classification

Thus, if some training document reduces the overall performance of text classification because of an outlier, we can assume that the document is a SV.

Text classification using machine learning (ML) techniques with a small number of labeled data has become more important with the rapid increase in volume of online documents.

In addition to regular text classification , we utilized topic modeling of the entire dataset in various ways.

Our proposed topic based classifier system is shown to be competitive with existing text classification techniques and provides a more efficient and interpretable representation.

2.3 Text Classification

Text classification is a supervised learning algorithm where documents’ categories are learned from pre-labeled set of documents.

SVM was chosen as the classification algorithm as it was shown that it performs well in text classification tasks (J oachims, 1998; Yang and Liu, 1999) and it is robust to overfitting (Sebastiani, 2002).

For text classification , topic modeling techniques have been utilized in various ways.

We showed that text classification based on Wikipedia cleanup templates is prone to a topic bias which causes skewed classifiers and overly optimistic cross-validated evaluation results.

This bias is known from other text classification applications, such as authorship attribution, genre detection and native language detection.

However, quality flaw detection based on cleanup template recognition suffers from a topic bias that is well known from other text classification applications such as authorship attribution or genre identification.

Topic bias is a known problem in text classification .

We exploit document-level geotags to indirectly generate training instances for text classifiers for toponym resolution, and show that textual cues can be straightforwardly integrated with other commonly used ones.

Essentially, we learn a text classifier per toponym.

Our results show these text classifiers are far more accurate than algorithms based on spatial proximity or metadata.

It learns text classifiers based on local context window features trained on instances automatically extracted from GEOWIKI.

Specifically, in our experiment, we assigned the term frequency, a feature frequently used in text classification to rn , and treated the phase 0" as a constant, since we found the phase makes little contribution to the classification.

We present here our model of text classification and compare it with SVM and KNN on two datasets.

Moreover, the QC performs well in text classification compared with SVM and KNN and outperforms them on small-scale training sets.

The motivation for this work was to test the hypothesis that information about word etymology is useful for computational approaches to language, in particular for text classification .

Cross-language text classification can be used to build comparable corpora in different languages, using a single language starting point, preferably one with more resources, that can thus spill over to other languages.

Text categorization (also text classification ), “the task of automatically sorting a set of documents into categories (or classes or topics) from a predefined set” (Sebastiani, 2005), allows for the quick selection of documents from the same domain, or the same topic.