Stefan Gindl

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.

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.

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.

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 companys 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 article goes beyond bipolar metrics of sentiment. It combines factual and affective knowledge extracted from rich public knowledge bases to analyze emotions expressed toward specific entities (targets) in social media. The authors obtain common and common-sense domain knowledge from DBpedia and ConceptNet to identify potential sentiment targets. They employ affective knowledge about emotional categories available from SenticNet to assess how those targets and their aspects (such as 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.

Dynamic topography information landscapes: an incremental approach to visual knowledge discovery

Incrementally computed information landscapes are an effective means to visualize longitudinal changes in large document repositories. Resembling tectonic processes in the natural world, dynamic rendering reflects both long-term trends and short-term fluctuations in such repositories. To visualize the rise and decay of topics, the mapping algorithm elevates and lowers related sets of concentric contour lines. Addressing the growing number of documents to be processed by state-of-the-art knowledge discovery applications, we introduce an incremental, scalable approach for generating such landscapes. The processing pipeline includes a number of sequential tasks, from crawling, filtering and pre-processing Web content to projecting, labeling and rendering the aggregated information. Incremental processing steps are localized in the projection stage consisting of document clustering, cluster force-directed placement and fast document positioning. We evaluate the proposed framework by contrasting layout qualities of incremental versus non-incremental versions. Documents for the experiments stem from the blog sample of the Media Watch on Climate Change (www.ecoresearch.net/climate). Experimental results indicate that our incremental computation approach is capable of accurately generating dynamic information landscapes.

Extracting Opinion Targets from Environmental Web Coverage and Social Media Streams

Policy makers and environmental organizations have a keen interest in awareness building and the evolution of stakeholder opinions on environmental issues. Mere polarity detection, as provided by many existing methods, does not suffice to understand the emergence of collective awareness. Methods for extracting affective knowledge should be able to pinpoint opinion targets within a thread. Opinion target extraction provides a more accurate and fine-grained identification of opinions expressed in online media. This paper compares two different approaches for identifying potential opinion targets and applies them to comments from the YouTube video sharing platform. The first approach is based on statistical keyword analysis in conjunction with sentiment classification on the sentence level. The second approach uses dependency parsing to pinpoint the target of an opinionated term. A case study based on YouTube postings applies the developed methods and measures their ability to handle noisy input data from social media streams.

Generic high-throughput methods for multilingual sentiment detection

Digital ecosystems typically involve a large number of participants from different sectors who generate rapidly growing archives of unstructured text. Measuring the frequency of certain terms to determine the popularity of a topic is comparably straightforward. Detecting sentiment expressed in user-generated electronic content is more challenging, especially in the case of digital ecosystems comprising heterogeneous sets of multilingual documents. This paper describes the use of language-specific grammar patterns and multilingual tagged dictionaries to detect sentiment in German and English document repositories. Digital ecosystems may contain millions of frequently updated documents, requiring sentiment detection methods that maximize throughput. The ideal combination of high-throughput techniques and more accurate (but slower) approaches depends on the specific requirements of an application. To accommodate a wide range of possible applications, this paper presents (i) an adaptive method, balancing accuracy and scalability of multilingual textual sources, (ii) a generic approach for generating language- specific grammar patterns and multilingual tagged dictionaries, and (iii) an extensive evaluation verifying the methods performance based on Amazon product reviews and user evaluations from Sentiment Quiz, a “game with a purpose” that invites users of the Facebook social networking platform to assess the sentiment of individual sentences.

DMOs’ Facebook Success Stories: A Retrospective View

Online marketing strategies are an important part of any destination promotion agenda. DMOs use Facebook to engage with various stakeholders and enhance their image. Given the benefits of this approach, destination managers often are not guided or well informed about success strategies to maintain their Facebook brand pages and effectively communicate, engage and enhance their relationships with their consumers. The lack of empirical longitudinal research led this study to perform a retrospective analysis of the Facebook pages of the 22 most popular tourist destinations in Europe according to TripAdvisor 2017 rankings. The data-driven approach demonstrates which marketing activities triggered various consumer engagement behaviour and, thus, are successful in Facebook spheres. Furthermore, the study allows destinations to benchmark their Facebook presence and position themselves more strategically.

Do DMOs Communicate Their Emotional Brand Values? A Comparison Between Twitter and Facebook

Communication through social media is an effective way to position a destination brand. In particular, the emotional values of a brand trigger a positive reaction from potential visitors. It is important for destinations to align their emotional communication strategies on different social media platforms to enhance their online image. The lack of comprehensive research in this area led this study to analyse the usage of the two most used social media platforms (Facebook and Twitter) among popular European tourist destinations. The study shows how destinations communicate their emotional values differently in Facebook and Twitter. The methodology of analysis allows destinations to compare the values they communicate with those of their competitors, so that they can improve their positioning and present a distinctive attractive personality of their destination.