Albert Weichselbraun

Extracting and Grounding Contextualized Sentiment Lexicons

A context-aware approach based on machine learning and lexical analysis identifies ambiguous terms and stores them in contextualized sentiment lexicons, which ground the terms to concepts corresponding to their polarity.

Enriching semantic knowledge bases for opinion mining in big data applications

This paper presents a novel method for contextualizing and enriching large semantic knowledge bases for opinion mining with a focus on Web intelligence platforms and other high-throughput big data applications. The method is not only applicable to traditional sentiment lexicons, but also to more comprehensive, multi-dimensional affective resources such as SenticNet. It comprises the following steps: (i) identify ambiguous sentiment terms, (ii) provide context information extracted from a domain-specific training corpus, and (iii) ground this contextual information to structured background knowledge sources such as ConceptNet and WordNet. A quantitative evaluation shows a significant improvement when using an enriched version of SenticNet for polarity classification. Crowdsourced gold standard data in conjunction with a qualitative evaluation sheds light on the strengths and weaknesses of the concept grounding, and on the quality of the enrichment process.

An Automated Approach to Investigating the Online Media Coverage of US Presidential Elections

ABSTRACT This paper presents the U.S. Election 2004 Web Monitor, a public Web portal that captured trends in political media coverage before and after the 2004 U.S. presidential election. Developed by the authors of this article, the webLyzard suite of Web mining tools provided the required functionality to aggregate and analyze about a half-million documents in weekly intervals. The study paid particular attention to the editorial slant, which is defined as the quantity and tone of a Web sites coverage as influenced by its editorial position. The observable attention and attitude toward the candidates served as proxies of editorial slant. The system identified attention by determining the frequency of candidate references and measured attitude towards the candidate by looking for positive and negative expressions that co-occur with these references. Keywords and perceptual maps summarized the most important topics associated with the candidates, placing special emphasis on environmental issues.

Multiple coordinated views for searching and navigating Web content repositories

The advantages and positive effects of multiple coordinated views on search performance have been documented in several studies. This paper describes the implementation of multiple coordinated views within the Media Watch on Climate Change, a domain-specific news aggregation portal available at www.ecoresearch.net/climate that combines a portfolio of semantic services with a visual information exploration and retrieval interface. The system builds contextualized information spaces by enriching the content repository with geospatial, semantic and temporal annotations, and by applying semi-automated ontology learning to create a controlled vocabulary for structuring the stored information. Portlets visualize the different dimensions of the contextualized information spaces, providing the user with multiple views on the latest news media coverage. Context information facilitates access to complex datasets and helps users navigate large repositories of Web documents. Currently, the system synchronizes information landscapes, domain ontologies, geographic maps, tag clouds and just-in-time information retrieval agents that suggest similar topics and nearby locations.

Refining non-taxonomic relation labels with external structured data to support ontology learning

This paper presents a method to integrate external knowledge sources such as DBpedia and OpenCyc into an ontology learning system that automatically suggests labels for unknown relations in domain ontologies based on large corpora of unstructured text. The method extracts and aggregates verb vectors from semantic relations identified in the corpus. It composes a knowledge base which consists of (i) verb centroids for known relations between domain concepts, (ii) mappings between concept pairs and the types of known relations, and (iii) ontological knowledge retrieved from external sources. Applying semantic inference and validation to this knowledge base improves the quality of suggested relation labels. A formal evaluation compares the accuracy and average ranking precision of this hybrid method with the performance of methods that solely rely on corpus data and those that are only based on reasoning and external data sources.

Using games with a purpose and bootstrapping to create domain-specific sentiment lexicons

Sentiment detection analyzes the positive or negative polarity of text. The field has received considerable attention in recent years, since it plays an important role in providing means to assess user opinions regarding an organizations products, services, or actions. Approaches towards sentiment detection include machine learning techniques as well as computationally less expensive methods. Both approaches rely on the use of language-specific sentiment lexicons, which are lists of sentiment terms with their corresponding sentiment value. The effort involved in creating, customizing, and extending sentiment lexicons is considerable, particularly if less common languages and domains are targeted without access to appropriate language resources. This paper proposes a semi-automatic approach for the creation of sentiment lexicons which assigns sentiment values to sentiment terms via crowd-sourcing. Furthermore, it introduces a bootstrapping process operating on unlabeled domain documents to extend the created lexicons, and to customize them according to the particular use case. This process considers sentiment terms as well as sentiment indicators occurring in the discourse surrounding a articular topic. Such indicators are associated with a positive or negative context in a particular domain, but might have a neutral connotation in other domains. A formal evaluation shows that bootstrapping considerably improves the methods recall. Automatically created lexicons yield a performance comparable to professionally created language resources such as the General Inquirer.

From Web Intelligence to Knowledge Co-Creation: A Platform for Analyzing and Supporting Stakeholder Communication

Organizations require tools that can assess their online reputations 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. Here, the authors introduce 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. The system computes positive or negative sentiment automatically to provide 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.

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.

Discovery and evaluation of non-taxonomic relations in domain ontologies

The identification and labelling of non-hierarchical relations are among the most challenging tasks in ontology learning. This paper describes a bottom-up approach for automatically suggesting ontology link types. The presented method extracts verb-vectors from semantic relations identified in the domain corpus, aggregates them by computing centroids for known relation types, and stores the centroids in a central knowledge base. Comparing verb-vectors extracted from unknown relations with the stored centroids yields link type suggestions. Domain experts evaluate these suggestions, refining the knowledge base and constantly improving the components accuracy. A final evaluation provides a detailed statistical analysis of the introduced approach.

Analyzing the public discourse on works of fiction - Detection and visualization of emotion in online coverage about HBO's Game of Thrones

Highlights • “Westeros Sentinel” – a visual analytics dashboard for Game of Thrones.• Extraction of affective and factual knowledge from news and social media coverage.• Emotional categories from semantic knowledge bases.• Automated annotation services for contextualized information spaces.• Interactive visualizations to explore context features.