Using online linear classifiers to filter spam Emails

The performance of two online linear classifiers - the Perceptron and Littlestone’s Winnow – is explored for two anti-spam filtering benchmark corpora - PU1 and Ling-Spam. We study the performance for varying numbers of features, along with three different feature selection methods: Information Gain (IG), Document Frequency (DF) and Odds Ratio. The size of the training set and the number of training iterations are also investigated for both classifiers. The experimental results show that both the Perceptron and Winnow perform much better when using IG or DF than using Odds Ratio. It is further demonstrated that when using IG or DF, the classifiers are insensitive to the number of features and the number of training iterations, and not greatly sensitive to the size of training set. Winnow is shown to slightly outperform the Perceptron. It is also demonstrated that both of these online classifiers perform much better than a standard Naïve Bayes method. The theoretical and implementation computational complexity of these two classifiers are very low, and they are very easily adaptively updated. They outperform most of the published results, while being significantly easier to train and adapt. The analysis and promising experimental results indicate that the Perceptron and Winnow are two very competitive classifiers for anti-spam filtering

Feature enrichment through multi-gram models

We introduce a feature enrichment approach, by developing multi-gram cosine similarity classification models. Our approach combines cosine similarity features of different N-gram word models, and unsupervised sentiment features, into models with a richer feature set than any of the approaches alone can provide. We test the classification models using different machine learning algorithms on categories of hateful and violent web content, and show that our multi-gram models give across-the-board performance improvements, for all categories tested, compared to combinations of baseline unigram, N-gram, and sentiment classification models. Our multi-gram models perform significantly better on highly imbalanced sets than the comparison methods, while this enrichment approach leaves room for further improvements, by adding instead of exhausting optimization options

A Study in Rule-Specific Issue Categorization for e-Rulemaking

We address the e-rulemaking problem of categorizing public comments according to the issues that they address. In contrast to previous text categorization research in e-rulemaking [5, 6], and in an attempt to more closely duplicate the comment analysis process in federal agencies, we employ a set of rule-specific categories, each of which corresponds to a significant issue raised in the comments. We describe the creation of a corpus to support this text categorization task and report interannotator agreement results for a group of six annotators. We outline those features of the task and of the e-rulemaking context that engender both a non-traditional text categorization corpus and a correspondingly difficult machine learning problem. Finally, we investigate the application of standard and hierarchical text categorization techniques to the e-rulemaking data sets and find that automatic categorization methods show promise as a means of reducing the manual labor required to analyze large comment sets: the automatic annotation methods approach the performance of human annotators for both flat and hierarchical issue categorization

Online suicide prevention through optimised text classification

Online communication platforms are increasingly used to express suicidal thoughts. There is considerable interest in monitoring such messages, both for population-wide and individual prevention purposes, and to inform suicide research and policy. Online information overload prohibits manual detection, which is why keyword search methods are typically used. However, these are imprecise and unable to handle implicit references or linguistic noise. As an alternative, this study investigates supervised text classification to model and detect suicidality in Dutch-language forum posts. Genetic algorithms were used to optimise models through feature selection and hyperparameter optimisation. A variety of features was found to be informative, including token and character ngram bags-of-words, presence of salient suicide-related terms and features based on LSA topic models and polarity lexicons. The results indicate that text classification is a viable and promising strategy for detecting suicide-related and alarming messages, with F-scores comparable to human annotators (93% for relevant messages, 70% for severe messages). Both types of messages can be detected with high precision and minimal noise, even on large high-skew corpora. This suggests that they would be fit for use in a real-world prevention setting

Advanced Text Analytics and Machine Learning Approach for Document Classification

Text classification is used in information extraction and retrieval from a given text, and text classification has been considered as an important step to manage a vast number of records given in digital form that is far-reaching and expanding. This thesis addresses patent document classification problem into fifteen different categories or classes, where some classes overlap with other classes for practical reasons. For the development of the classification model using machine learning techniques, useful features have been extracted from the given documents. The features are used to classify patent document as well as to generate useful tag-words. The overall objective of this work is to systematize NASA’s patent management, by developing a set of automated tools that can assist NASA to manage and market its portfolio of intellectual properties (IP), and to enable easier discovery of relevant IP by users. We have identified an array of methods that can be applied such as k-Nearest Neighbors (kNN), two variations of the Support Vector Machine (SVM) algorithms, and two tree based classification algorithms: Random Forest and J48. The major research steps in this work consist of filtering techniques for variable selection, information gain and feature correlation analysis, and training and testing potential models using effective classifiers. Further, the obstacles associated with the imbalanced data were mitigated by adding synthetic data wherever appropriate, which resulted in a superior SVM classifier based model

Advanced Text Analytics and Machine Learning Approach for Document Classification

Text classification is used in information extraction and retrieval from a given text, and text classification has been considered as an important step to manage a vast number of records given in digital form that is far-reaching and expanding. This thesis addresses patent document classification problem into fifteen different categories or classes, where some classes overlap with other classes for practical reasons. For the development of the classification model using machine learning techniques, useful features have been extracted from the given documents. The features are used to classify patent document as well as to generate useful tag-words. The overall objective of this work is to systematize NASA’s patent management, by developing a set of automated tools that can assist NASA to manage and market its portfolio of intellectual properties (IP), and to enable easier discovery of relevant IP by users. We have identified an array of methods that can be applied such as k-Nearest Neighbors (kNN), two variations of the Support Vector Machine (SVM) algorithms, and two tree based classification algorithms: Random Forest and J48. The major research steps in this work consist of filtering techniques for variable selection, information gain and feature correlation analysis, and training and testing potential models using effective classifiers. Further, the obstacles associated with the imbalanced data were mitigated by adding synthetic data wherever appropriate, which resulted in a superior SVM classifier based model