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.
@cite_37: The increasing availability of digitized text presents enormous opportunities for social scientists. Yet hand coding many blogs, speeches, government records, newspapers, or other sources of unstructured text is infeasible. Although computer scientists have methods for automated content analysis, most are optimized to classify individual documents, whereas social scientists instead want generalizations about the population of documents, such as the proportion in a given category. Unfortunately, even a method with a high percent of individual documents correctly classified can be hugely biased when estimating category proportions. By directly optimizing for this social science goal, we develop a method that gives approximately unbiased estimates of category proportions even when the optimal classifier performs poorly. We illustrate with diverse data sets, including the daily expressed opinions of thousands of people about the U.S. presidency. We also make available software that implements our methods and large corpora of text for further analysis.
@cite_4: 
@cite_29: 
@cite_42: Based on ideas from Support Vector Machines (SVMs), Learning To Classify Text Using Support Vector Machines presents a new approach to generating text classifiers from examples. The approach combines high performance and efficiency with theoretical understanding and improved robustness. In particular, it is highly effective without greedy heuristic components. The SVM approach is computationally efficient in training and classification, and it comes with a learning theory that can guide real-world applications. Learning To Classify Text Using Support Vector Machines gives a complete and detailed description of the SVM approach to learning text classifiers, including training algorithms, transductive text classification, efficient performance estimation, and a statistical learning model of text classification. In addition, it includes an overview of the field of text classification, making it self-contained even for newcomers to the field. This book gives a concise introduction to SVMs for pattern recognition, and it includes a detailed description of how to formulate text-classification tasks for machine learning.
@cite_3: In his campaign for the U.S. presidency from 1975 to 1979, Ronald Reagan delivered over 1000 radio broadcasts. For over 600 of these we have direct evidence of Reagan's authorship. The aim of this study was to determine the authorship of 312 of the broadcasts for which no direct evidence is available. We addressed the prediction problem for speeches delivered in different epochs and we explored a wide range of off-the-shelf classification methods and fully Bayesian generative models. Eventually we produced separate sets of predictions using the most accurate classifiers, based on non-contextual words as well as on semantic features, for the 312 speeches of uncertain authorship. All the predictions agree on 135 of the "unknown" speeches, whereas the fully Bayesian models agree on an additional 154 of them. The magnitude of the posterior odds of authorship led us to conclude that Ronald Reagan drafted 167 speeches and was aided in the preparation of the remaining 145. Our inferences were not sensitive to "reasonable" variations in the sets of constants underlying the prior distributions, and the cross-validated accuracy of our best fully Bayesian model was above 90 percent in all cases. The agreement of multiple methods for predicting the authorship for the "unknown" speeches reinforced our confidence in the accuracy of our classifications.
@cite_24: Keywords, which we define as a sequence of one or more words, provide a compact representation of a document’s content. Ideally, keywords represent in condensed form the essential content of a document.
@cite_19: The support-vector network is a new learning machine for two-group classification problems. The machine conceptually implements the following idea: input vectors are non-linearly mapped to a very high-dimension feature space. In this feature space a linear decision surface is constructed. Special properties of the decision surface ensures high generalization ability of the learning machine. The idea behind the support-vector network was previously implemented for the restricted case where the training data can be separated without errors. We here extend this result to non-separable training data. High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated. We also compare the performance of the support-vector network to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
@cite_40: 1. ABSTRACT Text categorization – the assignment of natural language texts to one or more predefined categories based on their content – is an important component in many information organization and management tasks. We compare the effectiveness of five different automatic learning algorithms for text categorization in terms of learning speed, realtime classification speed, and classification accuracy. We also examine training set size, and alternative document representations. Very accurate text classifiers can be learned automatically from training examples. Linear Support Vector Machines (SVMs) are particularly promising because they are very accurate, quick to train, and quick to evaluate. 1.1
@cite_27: extraction is an efficient approach to managing an explosion of online text on the Web. Traditionally, an abstraction of the online text is constructed though keywords, which are extracted according to a certain importance measure. One such measure is their occurrence frequency. However, previous work has not considered another important factor: the diversity of the keywords. Therefore, the extracted keywords tend to crowd on one hot topic in the corpora while failing to cover other important topics. In this paper, we propose new algorithms to alleviate the disadvantages of these traditional methods for keyword extraction. Firstly, we propose to extract key phrases instead of keywords because phrases can effectively reduce the ambiguity of single words. Secondly, by leveraging latent semantic analysis, we can learn the related topics for each phrase as well as the distance among the phrases, so that the extracted phrases are able to cover more topics. To demonstrate the performance of our method, we conducted experiments on two open datasets: 20 Newsgroup and Reuters-21578.We design three novel evaluation metrics, based on which both qualitative and quantitative analyses shows that our proposed algorithm can be used to improve the key phrase extraction performance significantly.
@cite_31: Despite widespread recognition that aggregated summary statistics on international conflict and cooperation miss most of the complex interactions among nations, the vast majority of scholars continue to employ annual, quarterly, or (occasionally) monthly observations. Daily events data, coded from some of the huge volume of news stories produced by journalists, have not been used much for the past two decades. We offer some reason to change this practice, which we feel should lead to considerably increased use of these data. We address advances in event categorization schemes and software programs that automatically produce data by “reading” news stories without human coders. We design a method that makes it feasible, for the first time, to evaluate these programs when they are applied in areas with the particular characteristics of international conflict and cooperation data, namely event categories with highly unequal prevalences, and where rare events (such as highly conflictual actions) are of special interest. We use this rare events design to evaluate one existing program, and find it to be as good as trained human coders, but obviously far less expensive to use. For large-scale data collections, the program dominates human coding. Our new evaluative method should be of use in international relations, as well as more generally in the field of computational linguistics, for evaluating other automated information extraction tools. We believe that the data created by programs similar to the one we evaluated should see dramatically increased use in international relations research. To facilitate this process, we are releasing with this article data on 3.7 million international events, covering the entire world for the past decade.
@cite_25: 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.
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 .