Potential and Limits of Automated Classification of Big Data: A Case Study

This case study highlights the potentials and limits of big-data analyses of media sources compared to conventional, quantitative content analysis. In an FFG-funded multidisciplinary project in Austria (based on the KIRAS security research program), the software tool WebLyzard was used for an automated analysis of online news and social media sources (comments on articles, Facebook postings, and Twitter statements) in order to analyze the media representation of pressing societal issues and citizens’ perceptions of security. Frequency and sentiment analyses were carried out by two independent observers in parallel to the automated WebLyzard results. Specific articles on selected key topics like technology or Muslims in two major online newspapers in Austria (Der Standard and Kronen Zeitung) were counted, as were user comments, and both were evaluated according to different sentiment categories. The results indicate various weaknesses of the software leading to misinterpretations, and the automated analyses yield substantially different results compared to the sentiment analysis carried out by the two raters, especially for cynical or irrelevant statements. From a social-sciences methodological perspective, the results clearly show that methodology in our discipline should promote theory-based research, should counteract the attraction of superficial analyses of complex social issues, and should emphasize not only the potentials but also the dangers and risks associated with big data

Automatic Expansion of Domain-Specific Affective Models for Web Intelligence Applications

Sentic computing relies on well-defined affective models of different complexity - polarity to distinguish positive and negative sentiment, for example, or more nuanced models to capture expressions of human emotions. When used to measure communication success, even the most granular affective model combined with sophisticated machine learning approaches may not fully capture an organisation's strategic positioning goals. Such goals often deviate from the assumptions of standardised affective models. While certain emotions such as Joy and Trust typically represent desirable brand associations, specific communication goals formulated by marketing professionals often go beyond such standard dimensions. For instance, the brand manager of a television show may consider fear or sadness to be desired emotions for its audience. This article introduces expansion techniques for affective models, combining common and commonsense knowledge available in knowledge graphs with language models and affective reasoning, improving coverage and consistency as well as supporting domain-specific interpretations of emotions. An extensive evaluation compares the performance of different expansion techniques: (i) a quantitative evaluation based on the revisited Hourglass of Emotions model to assess performance on complex models that cover multiple affective categories, using manually compiled gold standard data, and (ii) a qualitative evaluation of a domain-specific affective model for television programme brands. The results of these evaluations demonstrate that the introduced techniques support a variety of embeddings and pre-trained models. The paper concludes with a discussion on applying this approach to other scenarios where affective model resources are scarce

Aspect-Based Extraction and Analysis of Affective Knowledge from Social Media Streams

Extracting and analyzing affective knowledge from social media in a structured manner is a challenging task. Decision makers require insights into the public perception of a company's products and services, as a strategic feedback channel to guide communication campaigns, and as an early warning system to quickly react in the case of unforeseen events. The approach presented in this paper goes beyond bipolar metrics of sentiment. It combines factual and affective knowledge extracted from rich public knowledge bases to analyze emotions expressed towards specific entities (targets) in social media. We obtain common and common-sense domain knowledge from DBpedia and ConceptNet to identify potential sentiment targets. We employ affective knowledge about emotional categories available from SenticNet to assess how those targets and their aspects (e.g. specific product features) are perceived in social media. An evaluation shows the usefulness and correctness of the extracted domain knowledge, which is used in a proof-of-concept data analytics application to investigate the perception of car brands on social media in the period between September and November 2015