We propose a general framework for topic-specific summarization of large text corpora, and illustrate how it can be used for analysis in two quite different contexts: an OSHA database of fatality and catastrophe reports (to facilitate surveillance for patterns in circumstances leading to injury or death) and legal decisions on workers' compensation claims (to explore relevant case law). Our summarization framework, built on sparse classification methods, is a compromise between simple word frequency based methods currently in wide use, and more heavyweight, model-intensive methods such as Latent Dirichlet Allocation (LDA). For a particular topic of interest (e.g., mental health disability, or chemical reactions), we regress a labeling of documents onto the high-dimensional counts of all the other words and phrases in the documents. The resulting small set of phrases found as predictive are then harvested as the summary. Using a branch-and-bound approach, this method can be extended to allow for phrases of arbitrary length, which allows for potentially rich summarization. We discuss how focus on the purpose of the summaries can inform choices of regularization parameters and model constraints. We evaluate this tool by comparing computational time and summary statistics of the resulting word lists to three other methods in the literature. We also present a new R package, textreg. Overall, we argue that sparse methods have much to offer text analysis, and is a branch of research that should be considered further in this context.
CCS is distinct from classification. Classification is focused on sorting documents, such as for attributing authorship @cite_29 @cite_3 or counting types of news events @cite_31 @cite_37 . Text classification has been attempted using a wide array of machine learning methods such as naive bayes, linear discriminant analysis, or support vector machines @cite_19 , which easily allow for large numbers of features (the words and phrases) to be incorporated. For comparisons of these different methods on text see @cite_25 , @cite_3 or @cite_40 . For SVMs as an approach in particular for text, see the book of @cite_42 . For such methods and these evaluations, however, the features themselves are not of primary interest, classification is. We instead attempt to extract meaning from documents by contrasting sets to each other. This is most similar to key phrase extraction, a literature in its own right. See, for example, @cite_24 , @cite_4 , or @cite_27 .
@cite_37 @cite_4 @cite_29 @cite_42 @cite_3 @cite_24 @cite_19 @cite_40 @cite_27 @cite_31 @cite_25
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Text classification methods have been evaluated on topic classification tasks. This thesis extends the empirical evaluation to emotion classification tasks in the literary domain. This study selects two literary text classification problems---the eroticism classification in Dickinson's poems and the sentimentalism classification in early American novels---as two cases for this evaluation. Both problems focus on identifying certain kinds of emotion---a document property other than topic. This study chooses two popular text classification algorithms---naive Bayes and Support Vector Machines (SVM), and three feature engineering options---stemming, stopword removal and statistical feature selection (Odds Ratio and SVM)---as the subjects of evaluation. This study aims to examine the effects of the chosen classifiers and feature engineering options on the two emotion classification problems, and the interaction between the classifiers and the feature engineering options. This thesis seeks empirical answers to the following research questions: (1) is SVM a better classifier than naive Bayes regarding classification accuracy, new literary knowledge discovery and potential for example-based retrieval? (2) is SVM a better feature selection method than Odds Ratio regarding feature reduction rate and classification accuracy improvement? (3) does stop word removal affect the classification performance? (4) does stemming affect the performance of classifiers and feature selection methods? Some of our conclusions are consistent with what are obtained in topic classification, such as Odds Ratio does not improve SVM performance and stop word removal might harm classification. Some conclusions contradict previous results, such as SVM does not beat naive Bayes in both cases. Some findings are new to this area---SVM and naive Bayes select top features in different frequency ranges; stemming might harm feature selection methods. These experiment results provide new insights to the relation between classification methods, feature engineering options and non-topic document properties. They also provide guidance for classification method selection in literary text classification applications. Machine learning for text classification is the cornerstone of document categorization, news filtering, document routing, and personalization. In text domains, effective feature selection is essential to make the learning task efficient and more accurate. This paper presents an empirical comparison of twelve feature selection methods (e.g. Information Gain) evaluated on a benchmark of 229 text classification problem instances that were gathered from Reuters, TREC, OHSUMED, etc. The results are analyzed from multiple goal perspectives-accuracy, F-measure, precision, and recall-since each is appropriate in different situations. The results reveal that a new feature selection metric we call 'Bi-Normal Separation' (BNS), outperformed the others by a substantial margin in most situations. This margin widened in tasks with high class skew, which is rampant in text classification problems and is particularly challenging for induction algorithms. A new evaluation methodology is offered that focuses on the needs of the data mining practitioner faced with a single dataset who seeks to choose one (or a pair of) metrics that are most likely to yield the best performance. From this perspective, BNS was the top single choice for all goals except precision, for which Information Gain yielded the best result most often. This analysis also revealed, for example, that Information Gain and Chi-Squared have correlated failures, and so they work poorly together. When choosing optimal pairs of metrics for each of the four performance goals, BNS is consistently a member of the pair---e.g., for greatest recall, the pair BNS + F1-measure yielded the best performance on the greatest number of tasks by a considerable margin. Objective A system that translates narrative text in the medical domain into structured representation is in great demand. The system performs three sub-tasks: concept extraction, assertion classification, and relation identification. @PARASPLIT Design The overall system consists of five steps: (1) pre-processing sentences, (2) marking noun phrases (NPs) and adjective phrases (APs), (3) extracting concepts that use a dosage-unit dictionary to dynamically switch two models based on Conditional Random Fields (CRF), (4) classifying assertions based on voting of five classifiers, and (5) identifying relations using normalized sentences with a set of effective discriminating features. @PARASPLIT Measurements Macro-averaged and micro-averaged precision, recall and F-measure were used to evaluate results. @PARASPLIT Results The performance is competitive with the state-of-the-art systems with micro-averaged F-measure of 0.8489 for concept extraction, 0.9392 for assertion classification and 0.7326 for relation identification. @PARASPLIT Conclusions The system exploits an array of common features and achieves state-of-the-art performance. Prudent feature engineering sets the foundation of our systems. In concept extraction, we demonstrated that switching models, one of which is especially designed for telegraphic sentences, improved extraction of the treatment concept significantly. In assertion classification, a set of features derived from a rule-based classifier were proven to be effective for the classes such as conditional and possible. These classes would suffer from data scarcity in conventional machine-learning methods. In relation identification, we use two-staged architecture, the second of which applies pairwise classifiers to possible candidate classes. This architecture significantly improves performance. In this paper, we consider the task of text categorization as a graph classification problem. By representing textual documents as graph-of-words instead of historical n-gram bag-of-words, we extract more discriminative features that correspond to long-distance n-grams through frequent subgraph mining. Moreover, by capitalizing on the concept of k-core, we reduce the graph representation to its densest part – its main core – speeding up the feature extraction step for little to no cost in prediction performances. Experiments on four standard text classification datasets show statistically significant higher accuracy and macro-averaged F1-score compared to baseline approaches.