Information extraction of +/-effect events to support opinion inference

Recently, work in NLP was initiated on a type of opinion inference that arises when opinions are expressed toward events which have positive or negative effects on entities, called +/-effect events. The ultimate goal is to develop a fully automatic system capable of recognizing inferred attitudes. To achieve its results, the inference system requires all instances of +/-effect events. Therefore, this dissertation focuses on +/-effect events to support opinion inference. To extract +/-effect events, we first need the list of +/-effect events. Due to significant sense ambiguity, our goal is to develop a sense-level rather than word-level lexicon. To handle sense-level information, WordNet is adopted. We adopt a graph-based method which is seeded by entries culled from FrameNet and then expanded by exploiting semantic relations in WordNet. We show that WordNet relations are useful for the polarity propagation in the graph model. In addition, to maximize the effectiveness of different types of information, we combine a graph-based method using WordNet relations and a standard classifier using gloss information. Further, we provide evidence that the model is an effective way to guide manual annotation to find +/-effect senses that are not in the seed set. To exploit the sense-level lexicons, we have to carry out word sense disambiguation. We present a knowledge-based +/-effect coarse-grained word sense disambiguation method based on selectional preferences via topic models. For more information, we first group senses, and then utilize topic models to model selectional preferences. Our experiments show that selectional preferences are helpful in our work. To support opinion inferences, we need to identify not only +/-effect events but also their affected entities automatically. Thus, we address both +/-effect event detection and affected entity identification. Since +/-effect events and their affected entities are closely related, instead of a pipeline system, we present a joint model to extract +/-effect events and their affected entities simultaneously. We demonstrate that our joint model is promising to extract +/-effect events and their affected entities jointly

SUBJECTIVITY WORD SENSE DISAMBIGUATION: A METHOD FOR SENSE-AWARE SUBJECTIVITY ANALYSIS

Subjectivity lexicons have been invaluable resources in subjectivity analysis and their creation has been an important topic. Many systems rely on these lexicons. For any subjectivity analysis system, which relies on a subjectivity lexicon, subjectivity sense ambiguity is a serious problem. Such systems will be misled by the presence of subjectivity clues used with objective senses called false hits. We believe that any type of subjectivity analysis system relying on lexicons will benefit from a sense-aware approach. We think sense-aware subjectivity analysis has been neglected mostly because of the concerns related to word sense disambiguation (WSD), the problem of automatically determining which sense of a word is activated by the use of the word in a particular context according to a sense-inventory. Although WSD is the perfect tool for sense-aware classification, trust in traditional fine-grained WSD as an enabling technology is not high due to previous mostly unsuccessful results. In this thesis, we investigate feasible and practical methods to avoid these false hits via sense-aware analysis. We define a new coarse-grained WSD task capturing the right semantic granularity specific to subjectivity analysis

Multilingual sentiment analysis in social media.

252 p.This thesis addresses the task of analysing sentiment in messages coming from social media. The ultimate goal was to develop a Sentiment Analysis system for Basque. However, because of the socio-linguistic reality of the Basque language a tool providing only analysis for Basque would not be enough for a real world application. Thus, we set out to develop a multilingual system, including Basque, English, French and Spanish.The thesis addresses the following challenges to build such a system:- Analysing methods for creating Sentiment lexicons, suitable for less resourced languages.- Analysis of social media (specifically Twitter): Tweets pose several challenges in order to understand and extract opinions from such messages. Language identification and microtext normalization are addressed.- Research the state of the art in polarity classification, and develop a supervised classifier that is tested against well known social media benchmarks.- Develop a social media monitor capable of analysing sentiment with respect to specific events, products or organizations

Multilingual sentiment analysis in social media.

252 p.This thesis addresses the task of analysing sentiment in messages coming from social media. The ultimate goal was to develop a Sentiment Analysis system for Basque. However, because of the socio-linguistic reality of the Basque language a tool providing only analysis for Basque would not be enough for a real world application. Thus, we set out to develop a multilingual system, including Basque, English, French and Spanish.The thesis addresses the following challenges to build such a system:- Analysing methods for creating Sentiment lexicons, suitable for less resourced languages.- Analysis of social media (specifically Twitter): Tweets pose several challenges in order to understand and extract opinions from such messages. Language identification and microtext normalization are addressed.- Research the state of the art in polarity classification, and develop a supervised classifier that is tested against well known social media benchmarks.- Develop a social media monitor capable of analysing sentiment with respect to specific events, products or organizations

Extrapolating Subjectivity Research to Other Languages

Socrates articulated it best, "Speak, so I may see you." Indeed, language represents an invisible probe into the mind. It is the medium through which we express our deepest thoughts, our aspirations, our views, our feelings, our inner reality. From the beginning of artificial intelligence, researchers have sought to impart human like understanding to machines. As much of our language represents a form of self expression, capturing thoughts, beliefs, evaluations, opinions, and emotions which are not available for scrutiny by an outside observer, in the field of natural language, research involving these aspects has crystallized under the name of subjectivity and sentiment analysis. While subjectivity classification labels text as either subjective or objective, sentiment classification further divides subjective text into either positive, negative or neutral. In this thesis, I investigate techniques of generating tools and resources for subjectivity analysis that do not rely on an existing natural language processing infrastructure in a given language. This constraint is motivated by the fact that the vast majority of human languages are scarce from an electronic point of view: they lack basic tools such as part-of-speech taggers, parsers, or basic resources such as electronic text, annotated corpora or lexica. This severely limits the implementation of techniques on par with those developed for English, and by applying methods that are lighter in the usage of text processing infrastructure, we are able to conduct multilingual subjectivity research in these languages as well. Since my aim is also to minimize the amount of manual work required to develop lexica or corpora in these languages, the techniques proposed employ a lever approach, where English often acts as the donor language (the fulcrum in a lever) and allows through a relatively minimal amount of effort to establish preliminary subjectivity research in a target language

User Review Analysis for Requirement Elicitation: Thesis and the framework prototype's source code

Online reviews are an important channel for requirement elicitation. However, requirement engineers face challenges when analysing online user reviews, such as data volumes, technical supports, existing techniques, and legal barriers. Juan Wang proposes a framework solving user review analysis problems for the purpose of requirement elicitation that sets up a channel from downloading user reviews to structured analysis data. The main contributions of her work are: (1) the thesis proposed a framework to solve the user review analysis problem for requirement elicitation; (2) the prototype of this framework proves its feasibility; (3) the experiments prove the effectiveness and efficiency of this framework. This resource here is the latest version of Juan Wang's PhD thesis "User Review Analysis for Requirement Elicitation" and all the source code of the prototype for the framework as the results of her thesis

Statistical learning techniques for text categorization with sparse labeled data

Many applications involve learning a supervised classifier from very few explicitly labeled training examples, since the cost of manually labeling the training data is often prohibitively high. For instance, we expect a good classifier to learn our interests from a few example books or movies we like, and recommend similar ones in the future, or we expect a search engine to give more personalized search results based on whatever little it learned about our past queries and clicked documents. There is thus a need for classification techniques capable of learning from sparse labeled data, by exploiting additional information about the classification task at hand (e.g., background knowledge) or by employing more sophisticated features (e.g., n-gram sequences, trees, graphs). In this thesis, we focus on two approaches for overcoming the bottleneck of sparse labeled data. We first propose the Inductive/Transductive Latent Model (ILM/TLM), which is a new generative model for text documents. ILM/TLM has various building blocks designed to facilitate the integration of background knowledge (e.g., unlabeled documents, ontologies of concepts, encyclopedia) into the process of learning from small training data. Our method can be used for inductive and transductive learning and achieves significant gains over state-of-the-art methods for very small training sets. Second, we propose Structured Logistic Regression (SLR), which is a new coordinate-wise gradient ascent technique for learning logistic regression in the space of all (word or character) sequences in the training data. SLR exploits the inherent structure of the n-gram feature space in order to automatically provide a compact set of highly discriminative n-gram features. Our detailed experimental study shows that while SLR achieves similar classification results to those of the state-of-the-art methods (which use all n-gram features given explicitly), it is more than an order of magnitude faster than its opponents. The techniques presented in this thesis can be used to advance the technologies for automatically and efficiently building large training sets, therefore reducing the need for spending human computation on this task.Viele Anwendungen benutzen Klassifikatoren, die auf dünn gesäten Trainingsdaten lernen müssen, da es oft aufwändig ist, Trainingsdaten zur Verfügung zu stellen. Ein Beispiel für solche Anwendungen sind Empfehlungssysteme, die auf der Basis von sehr wenigen Büchern oder Filmen die Interessen des Benutzers erraten müssen, um ihm ähnliche Bücher oder Filme zu empfehlen. Ein anderes Beispiel sind Suchmaschinen, die sich auf den Benutzer einzustellen versuchen, auch wenn sie bisher nur sehr wenig Information über den Benutzer in Form von gestellten Anfragen oder geklickten Dokumenten besitzen. Wir benötigen also Klassifikationstechniken, die von dünn gesäten Trainingsdaten lernen können. Dies kann geschehen, indem zusätzliche Information über die Klassifikationsaufgabe ausgenutzt wird (z.B. mit Hintergrundwissen) oder indem raffiniertere Merkmale verwendet werden (z.B. n-Gram-Folgen, Bäume oder Graphen). In dieser Arbeit stellen wir zwei Ansätze vor, um das Problem der dünn gesäten Trainingsdaten anzugehen. Als erstes schlagen wir das Induktiv-Transduktive Latente Modell (ILM/TLM) vor, ein neues generatives Modell für Text-Dokumente. Das ILM/TLM verfügt über mehrere Komponenten, die es erlauben, Hintergrundwissen (wie z.B. nicht Klassifizierte Dokumente, Konzeptontologien oder Enzyklopädien) in den Lernprozess mit einzubeziehen. Diese Methode kann sowohl für induktives als auch für transduktives Lernen eingesetzt werden. Sie schlägt die modernsten Alternativmethoden signifikant bei dünn gesäten Trainingsdaten. Zweitens schlagen wir Strukturierte Logistische Regression (SLR) vor, ein neues Gradientenverfahren zum koordinatenweisen Lernen von logistischer Regression im Raum aller Wortfolgen oder Zeichenfolgen in den Trainingsdaten. SLR nutzt die inhärente Struktur des n-Gram-Raums aus, um automatisch hoch-diskriminative Merkmale zu finden. Unsere detaillierten Experimente zeigen, dass SLR ähnliche Ergebnisse erzielt wie die modernsten Konkurrenzmethoden, allerdings dabei um mehr als eine Größenordnung schneller ist. Die in dieser Arbeit vorgestellten Techniken verbessern das Maschinelle Lernen auf dünn gesäten Trainingsdaten und verringern den Bedarf an manueller Arbeit

Semi-supervised learning and fairness-aware learning under class imbalance

With the advent of Web 2.0 and the rapid technological advances, there is a plethora of data in every field; however, more data does not necessarily imply more information, rather the quality of data (veracity aspect) plays a key role. Data quality is a major issue, since machine learning algorithms are solely based on historical data to derive novel hypotheses. Data may contain noise, outliers, missing values and/or class labels, and skewed data distributions. The latter case, the so-called class-imbalance problem, is quite old and still affects dramatically machine learning algorithms. Class-imbalance causes classification models to learn effectively one particular class (majority) while ignoring other classes (minority). In extend to this issue, machine learning models that are applied in domains of high societal impact have become biased towards groups of people or individuals who are not well represented within the data. Direct and indirect discriminatory behavior is prohibited by international laws; thus, there is an urgency of mitigating discriminatory outcomes from machine learning algorithms. In this thesis, we address the aforementioned issues and propose methods that tackle class imbalance, and mitigate discriminatory outcomes in machine learning algorithms. As part of this thesis, we make the following contributions: • Tackling class-imbalance in semi-supervised learning – The class-imbalance problem is very often encountered in classification. There is a variety of methods that tackle this problem; however, there is a lack of methods that deal with class-imbalance in the semi-supervised learning. We address this problem by employing data augmentation in semi-supervised learning process in order to equalize class distributions. We show that semi-supervised learning coupled with data augmentation methods can overcome class-imbalance propagation and significantly outperform the standard semi-supervised annotation process. • Mitigating unfairness in supervised models – Fairness in supervised learning has received a lot of attention over the last years. A growing body of pre-, in- and postprocessing approaches has been proposed to mitigate algorithmic bias; however, these methods consider error rate as the performance measure of the machine learning algorithm, which causes high error rates on the under-represented class. To deal with this problem, we propose approaches that operate in pre-, in- and post-processing layers while accounting for all classes. Our proposed methods outperform state-of-the-art methods in terms of performance while being able to mitigate unfair outcomes