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
2009330779 1914856875 2755457990 1983046573
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.