Open Access

In this article, we explore the feasibility of extracting suitable and unsuitable food items for particular health conditions from natural language text. We refer to this task as conditional healthiness classification. For that purpose, we annotate a corpus extracted from forum entries of a food-related website. We identify different relation types that hold between food items and health conditions going beyond a binary distinction of suitability and unsuitability and devise various supervised classifiers using different types of features. We examine the impact of different task-specific resources, such as a healthiness lexicon that lists the healthiness status of a food item and a sentiment lexicon. Moreover, we also consider task-specific linguistic features that disambiguate a context in which mentions of a food item and a health condition co-occur and compare them with standard features using bag of words, part-of-speech information and syntactic parses. We also investigate in how far individual food items and health conditions correlate with specific relation types and try to harness this information for classification.

Digitale Korpora haben die Voraussetzungen, unter denen sich Wissenschaftler mit der Erforschung von Sprachphänomenen beschäftigen, fundamental verändert. Umfangreiche Sammlungen geschriebener und gesprochener Sprache bilden mittlerweile die empirische Basis für mathematisch präzise Generalisierungen über zu beschreibende Wirklichkeitsausschnitte. Das Datenmaterial ist hochkomplex und besteht neben den Rohtexten aus diversen linguistischen Annotationsebenen sowie außersprachlichen Metadaten. Als unmittelbare Folge stellt sich die Konzeption adäquater Recherchelösungen als beträchtliche Herausforderung dar. Im vorliegenden Buch wird deshalb ein datenbankbasierter Ansatz vorgestellt, der sich der Problematiken multidimensionaler Korpusrecherchen annimmt. Ausgehend von einer Charakterisierung der Anforderungsmerkmale linguistisch motivierter Suchen werden Speicherungs- und Abfragestrategien für mehrfach annotierte Korpora entwickelt und anhand eines linguistischen Anforderungskatalogs evaluiert. Ein Schwerpunkt liegt dabei in der Einführung problemorientierter Segmentierung und Parallelisierung.

Sentiment Analysis is the task of extracting and classifying opinionated content in natural language texts. Common subtasks are the distinction between opinionated and factual texts, the classification of polarity in opinionated texts, and the extraction of the participating entities of an opinion(-event), i.e. the source from which an opinion emanates and the target towards which it is directed. With the emerging Web 2.0 which describes the shift towards a highly user-interactive communication medium, the amount of subjective content on the World Wide Web is steadily increasing. Thus, there is a growing need for automatically processing this type of content which is provided by sentiment analysis. Both natural language processing, which is the task of providing computational methods for the analysis and representation of natural language, and machine learning, which is the task of building task-specific classification models on the basis of empirical data, may be instrumental in mastering the challenges of the automatic sentiment analysis of written text. Many problems in sentiment analysis have been proposed to be solved with machine learning methods exclusively using a fairly low-level feature design, such as bag of words, containing little linguistic information. In this thesis, we examine the effectiveness of linguistic features in various subtasks of sentiment analysis. Thus, we heavily draw from the insights gained by natural language processing. The application of linguistic features can be applied on various classification methods, be it in rule-based classification, where the linguistic features are directly encoded as a classifier, in supervised machine learning, where these features complement basic low-level features, or in bootstrapping methods, where these features form a rule-based classifier generating a labeled training set from which a supervised classifier can be trained. In this thesis, we will in particular focus on scenarios where the combination of linguistic features and machine learning methods is effective. We will look at common text classification tasks, both coarse-grained and fine-grained, and extraction tasks.