Czech Text Document Corpus v 2.0

This paper introduces "Czech Text Document Corpus v 2.0", a collection of text documents for automatic document classification in Czech language. It is composed of the text documents provided by the Czech News Agency and is freely available for research purposes at http://ctdc.kiv.zcu.cz/. This corpus was created in order to facilitate a straightforward comparison of the document classification approaches on Czech data. It is particularly dedicated to evaluation of multi-label document classification approaches, because one document is usually labelled with more than one label. Besides the information about the document classes, the corpus is also annotated at the morphological layer. This paper further shows the results of selected state-of-the-art methods on this corpus to offer the possibility of an easy comparison with these approaches.Comment: Accepted for LREC 201

Representation and learning schemes for sentiment analysis.

This thesis identifies four novel techniques of improving the performance of sentiment analysis of text systems. Thes include feature extraction and selection, enrichment of the document representation and exploitation of the ordinal structure of rating classes. The techniques were evaluated on four sentiment-rich corpora, using two well-known classifiers: Support Vector Machines and Na¨ıve Bayes. This thesis proposes the Part-of-Speech Pattern Selector (PPS), which is a novel technique for automatically selecting Part-of-Speech (PoS) patterns. The PPS selects its patterns from a background dataset by use of a number of measures including Document Frequency, Information Gain, and the Chi-Squared Score. Extensive empirical results show that these patterns perform just as well as the manually selected ones. This has important implications in terms of both the cost and the time spent in manual pattern construction. The position of a phrase within a document is shown to have an influence on its sentiment orientation, and that document classification performance can be improved by weighting phrases in this regard. It is, however, also shown to be necessary to sample the distribution of sentiment rich phrases within documents of a given domain prior to adopting a phrase weighting criteria. A key factor in choosing a classifier for an Ordinal Sentiment Classification (OSC) problem is its ability to address ordinal inter-class similarities. Two types of classifiers are investigated: Those that can inherently solve multi-class problems, and those that decompose a multi-class problem into a sequence of binary problems. Empirical results showed the former to be more effective with regard to both mean squared error and classification time performances. Important features in an OSC problem are shown to distribute themselves across similar classes. Most feature selection techniques are ignorant of inter-class similarities and hence easily overlook such features. The Ordinal Smoothing Procedure (OSP), which augments inter-class similarities into the feature selection process, is introduced in this thesis. Empirical results show the OSP to have a positive effect on mean squared error performance

A comparative study of Bayesian models for unsupervised sentiment detection

This paper presents a comparative study of three closely related Bayesian models for unsupervised document level sentiment classification, namely, the latent sentiment model (LSM), the joint sentimenttopic (JST) model, and the Reverse-JST model. Extensive experiments have been conducted on two corpora, the movie review dataset and the multi-domain sentiment dataset. It has been found that while all the three models achieve either better or comparable performance on these two corpora when compared to the existing unsupervised sentiment classification approaches, both JST and Reverse-JST are able to extract sentiment-oriented topics. In addition, Reverse-JST always performs worse than JST suggesting that the JST model is more appropriate for joint sentiment topic detection

Learning to predict distributions of words across domains

Although the distributional hypothesis has been applied successfully in many natural language processing tasks, systems using distributional information have been limited to a single domain because the distribution of a word can vary between domains as the word’s predominant meaning changes. However, if it were possible to predict how the distribution of a word changes from one domain to another, the predictions could be used to adapt a system trained in one domain to work in another. We propose an unsupervised method to predict the distribution of a word in one domain, given its distribution in another domain. We evaluate our method on two tasks: cross-domain part-of-speech tagging and cross-domain sentiment classification. In both tasks, our method significantly outperforms competitive baselines and returns results that are statistically comparable to current state-of-the-art methods, while requiring no task-specific customisations

Latent sentiment model for weakly-supervised cross-lingual sentiment classification

In this paper, we present a novel weakly-supervised method for crosslingual sentiment analysis. In specific, we propose a latent sentiment model (LSM) based on latent Dirichlet allocation where sentiment labels are considered as topics. Prior information extracted from English sentiment lexicons through machine translation are incorporated into LSM model learning, where preferences on expectations of sentiment labels of those lexicon words are expressed using generalized expectation criteria. An efficient parameter estimation procedure using variational Bayes is presented. Experimental results on the Chinese product reviews show that the weakly-supervised LSM model performs comparably to supervised classifiers such as Support vector Machines with an average of 81% accuracy achieved over a total of 5484 review documents. Moreover, starting with a generic sentiment lexicon, the LSM model is able to extract highly domainspecific polarity words from text