Extracting Knowledge from the Web and Social Media for Progress Monitoring in Public Outreach and Science Communication

Given the intense attention that environmental topics such as climate change attract in news and social media coverage, key questions for large science agencies such as the National Oceanic and Atmospheric Administration (NOAA) are how different stakeholders perceive the observable threats and policy options, how public media react to new scientific insights, and how journalists present climate science knowledge to the public. This paper investigates the potential of semantic technologies to address these questions. It introduces the NOAA Media Watch and presents a detailed case study of how the metrics and visualizations of the webLyzard Web intelligence platform are used to track information flows across online media channels. Building upon this platform, we present a novel framework to measure the impact of science communication and public outreach campaigns – through a combination of quantitative and visual methods that go beyond sentiment analysis and related opinion mining approaches

Rule-based Opinion Target and Aspect Extraction to Acquire Affective Knowledge

Opinion holder and opinion target extraction are among the most popular and challenging problems tackled by opinion mining researchers, recognizing the significant business value of such components and their importance for applications such as media monitoring and Web intelligence. This paper describes an approach that combines opinion target extraction with aspect extraction using syntactic patterns. It expands previous work limited by sentence boundaries and includes a heuristic for anaphora resolution to identify targets across sentences. Furthermore, it demonstrates the application of concepts known from research on open information extraction to the identification of relevant opinion aspects. Qualitative analyses performed on a corpus of 100 000 Amazon product reviews show that the approach is promising. The extracted opinion targets and aspects are useful for enriching common knowledge resources and opinion mining ontologies, and support practitioners and researchers to identify opinions in document collections

Media Watch on Climate Change – Visual Analytics for Aggregating and Managing Environmental Knowledge from Online Sources

This paper presents the Media Watch on Climate Change, a public Web portal that captures and aggregates large archives of digital content from multiple stakeholder groups. Each week it assesses the domain-specific relevance of millions of documents and user comments from news media, blogs, Web 2.0 platforms such as Facebook, Twitter and YouTube, the Web sites of companies and NGOs, and a range of other sources. An interactive dashboard with trend charts and complex map projections not only shows how often and where environmental information is published, but also provides a real-time account of concepts that stakeholders associate with climate change. Positive or negative sentiment is computed automatically, which not only sheds light on the impact of education and public outreach campaigns that target environmental literacy, but also help to gain a better understanding of how others perceive climate-related issues

Extracting and Grounding Context-Aware Sentiment Lexicons

Web intelligence applications track online sources with economic relevance such as customer reviews, news articles and social media postings. Automated sentiment analysis based on lexical methods or machine learning identifies the polarity of opinions expressed in these sources to assess how stakeholders perceive a topic. This paper introduces a hybrid approach that combines the throughput of lexical analysis with the flexibility of machine learning to resolve ambiguity and consider the context of sentiment terms. The context-aware method identifies ambiguous terms that vary in polarity depending on the context and stores them in contextualized sentiment lexicons. In conjunction with semantic knowledge bases, these lexicons help ground ambiguous sentiment terms to concepts that correspond to their polarity. This grounding paves the way for interlinking, extending, or even replacing contextualized sentiment lexicons with semantic knowledge bases. An extensive evaluation applies the method to user reviews across three domains (movies, products and hotels)

From Web Intelligence to Knowledge Co-Creation – A Platform to Analyze and Support Stakeholder Communication

Organizations require tools to assess their online reputation as well as the impact of their marketing and public outreach activities. The Media Watch on Climate Change is a Web intelligence and online collaboration platform that addresses this requirement. It aggregates large archives of digital content from multiple stakeholder groups and enables the co-creation and visualization of evolving knowledge archives. This paper introduces the base platform and a context-aware document editor as an add-on that supports concurrent authoring by multiple users. While documents are being edited, semantic methods analyze them on the fly to recommend related content. Positive or negative sentiment is computed automatically to gain a better understanding of third-party perceptions. The editor is part of an interactive dashboard that uses trend charts and map projections to show how often and where relevant information is published, and to provide a real-time account of concepts that stakeholders associate with a topic

Dynamic Integration of Multiple Evidence Sources for Ontology Learning

Although ontologies are central to the Semantic Web, current ontology learning methods primarily make use of a single evidence source and are agnostic in their internal representations to the evolution of ontology knowledge. This article presents a continuous ontology learning framework that overcomes these shortcomings by integrating evidence from multiple, heterogeneous sources (unstructured, structured, social) in a consistent model, and by providing mechanisms for the fine-grained tracing of the evolution of domain ontologies. The presented framework supports a tight integration of human and machine computation. Crowdsourcing in the tradition of games with a purpose performs the evaluation of the learned ontologies and facilitates the automatic optimization of learning algorithms

TextSweeper - A System for Content Extraction and Overview Page Detection

Web pages not only contain main content, but also other elements such as navigation panels, advertisements and links to related documents. Furthermore, overview pages (summarization pages and entry points) duplicate and aggregate parts of articles and thereby create redundancies. The noise elements in Web pages as well as overview pages affect the performance of downstream processes such as Web-based Information Retrieval. Context Extraction's task is identifying and extracting the main content from a Web page. In this research-in-progress paper we present an approach which not only identifies and extracts the main content, but also detects overview pages and thereby allows skipping them. The content extraction part of the system is an extension of existing Text-to-Tag ratio methods, overview page detection is accomplished with the net text length heuristic. Preliminary results and ad-hoc evaluation indicate a promising system performance. A formal evaluation and comparison to other state-of-the-art approaches is part of future work