The SVM learning algorithm as implemented in the LIB SVM software package (Chang and Lin, 2001) is used for classifier training, owing to its robust performance on many text classification tasks.

Unlike newswire articles, at which many topic-based text classification tasks are targeted, the ASRS reports are informally written using various domain-specific abbreviations and acronyms, tend to contain poor grammar, and have capitalization information removed, as illustrated in the following sentence taken from one of the reports.

Automatic text classification is one of the most important applications in natural language processing (NLP).

The difficulty of a text classification task depends on various factors, but typically, the task can be difficult if (1) the amount of labeled data available for learning the task is small; (2) it involves multiple classes; (3) it involves multi-label categorization, where more than one label can be assigned to each document; (4) the class distributions are skewed, with some categories significantly outnumbering the others; and (5) the documents belong to the same domain (e. g., movie review classification).

In this paper, we introduce a new text classification problem involving the Aviation Safety Reporting System (ASRS) that can be viewed as a difficult task along each of the five dimensions discussed above.

Since we recast cause identification as a text classification task and proposed a bootstrapping approach that targets at improving minority class prediction, the work most related to ours involves one or both of these topics.

(2007) address the problem of class skewness in text classification .

Similar bootstrapping methods are applicable outside text classification as well.

We present a new approach to cross-language text classification that builds on structural correspondence learning, a recently proposed theory for domain adaptation.

This paper deals with cross-language text classification problems.

Stated precisely: We are given a text classification task 7 in a target language ’2' for which no labeled documents are available.

Such type of cross-language text classification problems are addressed by constructing a classifier f5 with training documents written in S and by applying f3 to unlabeled documents written in ’2'.

Cross-Language Text Classification Bel et al.

(2003) belong to the first who explicitly considered the problem of cross-language text classification .

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 .

Supervised text classification algorithms require a large number of documents labeled by humans, that involve a labor-intensive and time consuming process.

We evaluate this approach to improve performance of text classification on three real world datasets.

In supervised text classification learning algorithms, the learner (a program) takes human labeled documents as input and learns a decision function that can classify a previously unseen document to one of the predefined classes.

In this paper, we propose a text classification algorithm based on Latent Dirichlet Allocation (LDA) (Blei et al., 2003) which does not need labeled documents.

(Blei et al., 2003) used LDA topics as features in text classification , but they use labeled documents while learning a classifier.

Several researchers have proposed semi-supervised text classification algorithms with the aim of reducing the time, effort and cost involved in labeling documents.

Semi-supervised text classification algorithms proposed in (Nigam et al., 2000), (J oachims, 1999), (Zhu and Ghahra—mani, 2002) and (Blum and Mitchell, 1998) are a few examples of this type.

Also a human annotator may discard or mislabel a polysemous word, which may affect the performance of a text classifier .

This paper explores a text classification problem we will call lect modeling, an example of what has been termed computational sociolinguistics.

Our results validate the treatment of lect modeling as a text classification problem — albeit a hard one — and constitute a case for future research in computational sociolinguistics.

Given, then, that there are distinct differences among what we term UpSpeak and DownSpeak, we treat Social Power Modeling as an instance of text classification (or categorization): we seek to assign a class (UpSpeak or DownSpeak) to a text sample.

2.2 Cross-Domain Text Classification

Cross-domain text classification can be considered as a more general task than cross-lingual sentiment classification.

In the problem of cross-domain text classification , the labeled and unlabeled data come from different domains, and their underlying distributions are often different from each other, which violates the basic assumption of traditional classification learning.

Typical text classifiers include Support Vector Machine (SVM), Na'1've Bayes (NB), Maximum Entropy (ME), K-Nearest Neighbor (KNN), etc.

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.

This makes it applicable to other text classification tasks.

Unlike topic-based text classification , where a high accuracy can be achieved even for datasets with a large number of classes (e.g., 20 Newsgroups), polarity classification appears to be a more difficult task.

One reason topic-based text classification is easier than polarity classification is that topic clusters are typically well-separated from each other, resulting from the fact that word usage differs considerably between two topically-different documents.

Implicitly or explicitly, previous work has mostly treated automated assessment as a supervised text classification task, where training texts are labelled with a grade and unlabelled test texts are fitted to the same grade point scale via a regression step applied to the classifier output (see Section 6 for more details).

Discriminative classification techniques often outperform non-discriminative ones in the context of text classification (J oachims, 1998).

This system shows that treating AA as a text classification problem is viable, but the feature types are all fairly shallow, and the approach doesn’t make efficient use of the training data as a separate classifier is trained for each grade point.

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.

The H-groups shown in Table 1 provide richer semantic descriptions of the domain than keywords do, and we noted potential applications for high-level summarization of a whole corpus, the creation of information extraction templates and finer- grained text classification and retrieval.

For broad topics it is desirable to perform f1ner- grained text classification and retrieval.

The alternation in V-groups contained by H-groups may reflect different beliefs and opinions which could be used for text classification and opinion mining.