The automatic content analysis of mass media in the social sciences has become necessary and possible with the raise of social media and computational power. One particularly promising avenue of research concerns the use of opinion mining. We design and implement the POPmine system which is able to collect texts from web-based conventional media (news items in mainstream media sites) and social media (blogs and Twitter) and to process those texts, recognizing topics and political actors, analyzing relevant linguistic units, and generating indicators of both frequency of mention and polarity (positivity negativity) of mentions to political actors across sources, types of sources, and across time.
Content analysis of mass media has an established tradition in the social sciences, particularly in the study of effects of media messages, encompassing topics as diverse as those addressed in seminal studies of newspaper editorials @cite_7 , media agenda-setting @cite_15 , or the uses of political rhetoric @cite_6 , among many others. By 1997, Riffe and Freitag @cite_13 , reported an increase in the use of content analysis in communication research and suggested that digital text and computerized means for its extraction and analysis would reinforce such trend. Their expectation has been fulfilled: the use of automated content analysis has by now surpassed the use of hand coding @cite_3 . The increase in the digital sources of text, on the one hand, and current advances in computation power and design, on the other, are making this development both necessary and possible, while also raising awareness about the inferential pitfalls involved @cite_11 @cite_8 .
@cite_7 @cite_8 @cite_3 @cite_6 @cite_15 @cite_13 @cite_11
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Examination of the increasing number of articles employing quantitative content analysis in 1971–95 Journalism & Mass Communication Quarterly showed primary focus on news editorial content in U.S. media. Nearly half examined newspapers, and half were coauthored. Most used convenience or purposive samples. Few involved a second research method or extra-media data, explicit theoretical grounding, or research questions or hypotheses. Half reported intercoder reliability, and two-fifths used only descriptive statistics. Analysis of trends shows growth in coauthorship and reporting of reliability, and increasing emphasis on more sophisticated statistical analysis. No parallel trend exists, however, in use of explicit hypotheses research questions or theoretical grounding. While the study of the connection between discourse patterns and personal identification is decades old, the study of these patterns using language technologies is relatively recent. In that more recent tradition we frame author age prediction from text as a regression problem. We explore the same task using three very different genres of data simultaneously: blogs, telephone conversations, and online forum posts. We employ a technique from domain adaptation that allows us to train a joint model involving all three corpora together as well as separately and analyze differences in predictive features across joint and corpus-specific aspects of the model. Effective features include both stylistic ones (such as POS patterns) as well as content oriented ones. Using a linear regression model based on shallow text features, we obtain correlations up to 0.74 and mean absolute errors between 4.1 and 6.8 years. Media data has been the subject of large scale analysis with applications of text mining being used to provide overviews of media themes and information flows. Such information extracted from media articles has also shown its contextual value of being integrated with other data, such as criminal records and stock market pricing. In this work, we explore linking textual media data with curated secondary textual data sources through user-guided semantic lexical matching for identifying relationships and data links. In this manner, critical information can be identified and used to annotate media timelines in order to provide a more detailed overview of events that may be driving media topics and frames. These linked events are further analyzed through an application of causality modeling to model temporal drivers between the data series. Such causal links are then annotated through automatic entity extraction which enables the analyst to explore persons, locations, and organizations that may be pertinent to the media topic of interest. To demonstrate the proposed framework, two media datasets and an armed conflict event dataset are explored. The recent surge in the usage of social media has created an enormous amount of user-generated content (UGC). While there are streams of research that seek to mine UGC, these research studies seldom tackle analysis of this textual content from a quality management perspective. In this study, we synthesize existing research studies on text mining and propose an integrated text analytic framework for product defect discovery. The framework effectively leverages rich social media content and quantifies the text using various automatically extracted signal cues. These extracted signal cues can then be used as modeling inputs for product defect discovery. We showcase the usefulness of the framework by performing product defect discovery using UGC in both the automotive and the consumer electronics domains. We use principal component analysis and logistic regression to produce a multivariate explanatory analysis relating defects to quantitative measures derived from text. For our samples, we find that a selection of distinctive terms, product features, and semantic factors are strong indicators of defects, whereas stylistic, social, and sentiment features are not. For high sales volume products, we demonstrate that significant corporate value is derivable from a reduction in defect discovery time and consequently defective product units in circulation. levels, such as Pet6fi's text grammar and Rumelhart's schemata for stories. By defining permissible interrelations between constituents of larger texts at all levels of abstraction, these create meaning structures organized on both hierarchical and associative principles, rather than on a sequential basis. The automated "understanding" process consists of instantiating an appropriate set of event templates by associating a given text with the ERGO inventory of event templates and const ucting a network of instanti ted templates based on intertemplate relational rules and constraints. The network of instantiated t mplates thus represents the information content of the unstructured original text, and provides the structured input for the event record data bas . Details of the automated understanding process presented here in the abstract are given in the follo ing section. 3.2 Processing Principles. The process of data base generation involves two major functions: (1) co tent analysis of the incoming text (2) event record synthesis (or production) The first involves constructing a meaning representation of the text and the second the extraction of relevant information and its storage in a data base record. The major focus of ERGO is on reports of a particular class of events which describe aircraft movements. The unit of analysis is therefore the report, a textual unit consisting of one or more par graphs, each containing one or more sentences. The first step in the analytical process involves a lexical lookup: a lexical entry contains morphological, syntactic and semantic information, on the lines of Sager (1973) and (1973). Each sentence is then subjected to a syntactic analysis by means of an Augmented Transition Network (ATN) parser (Woods, 1970). Since event templates are based on propositional structures, the analytical In this paper we introduce the novel problem of understanding visual persuasion. Modern mass media make extensive use of images to persuade people to make commercial and political decisions. These effects and techniques are widely studied in the social sciences, but behavioral studies do not scale to massive datasets. Computer vision has made great strides in building syntactical representations of images, such as detection and identification of objects. However, the pervasive use of images for communicative purposes has been largely ignored. We extend the significant advances in syntactic analysis in computer vision to the higher-level challenge of understanding the underlying communicative intent implied in images. We begin by identifying nine dimensions of persuasive intent latent in images of politicians, such as "socially dominant, " "energetic, " and "trustworthy, " and propose a hierarchical model that builds on the layer of syntactical attributes, such as "smile" and "waving hand, " to predict the intents presented in the images. To facilitate progress, we introduce a new dataset of 1, 124 images of politicians labeled with ground-truth intents in the form of rankings. This study demonstrates that a systematic focus on visual persuasion opens up the field of computer vision to a new class of investigations around mediated images, intersecting with media analysis, psychology, and political communication. 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.