Doctor of Philosophy in Computer Science

dissertationOver the last decade, social media has emerged as a revolutionary platform for informal communication and social interactions among people. Publicly expressing thoughts, opinions, and feelings is one of the key characteristics of social media. In this dissertation, I present research on automatically acquiring knowledge from social media that can be used to recognize people's affective state (i.e., what someone feels at a given time) in text. This research addresses two types of affective knowledge: 1) hashtag indicators of emotion consisting of emotion hashtags and emotion hashtag patterns, and 2) affective understanding of similes (a form of figurative comparison). My research introduces a bootstrapped learning algorithm for learning hashtag in- dicators of emotions from tweets with respect to five emotion categories: Affection, Anger/Rage, Fear/Anxiety, Joy, and Sadness/Disappointment. With a few seed emotion hashtags per emotion category, the bootstrapping algorithm iteratively learns new hashtags and more generalized hashtag patterns by analyzing emotion in tweets that contain these indicators. Emotion phrases are also harvested from the learned indicators to train additional classifiers that use the surrounding word context of the phrases as features. This is the first work to learn hashtag indicators of emotions. My research also presents a supervised classification method for classifying affective polarity of similes in Twitter. Using lexical, semantic, and sentiment properties of different simile components as features, supervised classifiers are trained to classify a simile into a positive or negative affective polarity class. The property of comparison is also fundamental to the affective understanding of similes. My research introduces a novel framework for inferring implicit properties that 1) uses syntactic constructions, statistical association, dictionary definitions and word embedding vector similarity to generate and rank candidate properties, 2) re-ranks the top properties using influence from multiple simile components, and 3) aggregates the ranks of each property from different methods to create a final ranked list of properties. The inferred properties are used to derive additional features for the supervised classifiers to further improve affective polarity recognition. Experimental results show substantial improvements in affective understanding of similes over the use of existing sentiment resources

Bootstrap domain-specific sentiment classifiers from unlabeled corpora

There is often the need to perform sentiment classification in a particular domain where no labeled document is available. Although we could make use of a general-purpose off-the-shelf sentiment classifier or a pre-built one for a different domain, the effectiveness would be inferior. In this paper, we explore the possibility of building domain-specific sentiment classifiers with unlabeled documents only. Our investigation indicates that in the word embeddings learned from the unlabeled corpus of a given domain, the distributed word representations (vectors) for opposite sentiments form distinct clusters, though those clusters are not transferable across domains. Exploiting such a clustering structure, we are able to utilize machine learning algorithms to induce a quality domain-specific sentiment lexicon from just a few typical sentiment words ("seeds"). An important finding is that simple linear model based supervised learning algorithms (such as linear SVM) can actually work better than more sophisticated semi-supervised/transductive learning algorithms which represent the state-of-the-art technique for sentiment lexicon induction. The induced lexicon could be applied directly in a lexicon-based method for sentiment classification, but a higher performance could be achieved through a two-phase bootstrapping method which uses the induced lexicon to assign positive/negative sentiment scores to unlabeled documents first, and then uses those documents found to have clear sentiment signals as pseudo-labeled examples to train a document sentiment classifier via supervised learning algorithms (such as LSTM). On several benchmark datasets for document sentiment classification, our end-to-end pipelined approach which is overall unsupervised (except for a tiny set of seed words) outperforms existing unsupervised approaches and achieves an accuracy comparable to that of fully supervised approaches

We usually don't like going to the dentist : using common sense to detect irony on Twitter

A

Semi-Supervised Learning For Identifying Opinions In Web Content

Thesis (Ph.D.) - Indiana University, Information Science, 2011Opinions published on the World Wide Web (Web) offer opportunities for detecting personal attitudes regarding topics, products, and services. The opinion detection literature indicates that both a large body of opinions and a wide variety of opinion features are essential for capturing subtle opinion information. Although a large amount of opinion-labeled data is preferable for opinion detection systems, opinion-labeled data is often limited, especially at sub-document levels, and manual annotation is tedious, expensive and error-prone. This shortage of opinion-labeled data is less challenging in some domains (e.g., movie reviews) than in others (e.g., blog posts). While a simple method for improving accuracy in challenging domains is to borrow opinion-labeled data from a non-target data domain, this approach often fails because of the domain transfer problem: Opinion detection strategies designed for one data domain generally do not perform well in another domain. However, while it is difficult to obtain opinion-labeled data, unlabeled user-generated opinion data are readily available. Semi-supervised learning (SSL) requires only limited labeled data to automatically label unlabeled data and has achieved promising results in various natural language processing (NLP) tasks, including traditional topic classification; but SSL has been applied in only a few opinion detection studies. This study investigates application of four different SSL algorithms in three types of Web content: edited news articles, semi-structured movie reviews, and the informal and unstructured content of the blogosphere. SSL algorithms are also evaluated for their effectiveness in sparse data situations and domain adaptation. Research findings suggest that, when there is limited labeled data, SSL is a promising approach for opinion detection in Web content. Although the contributions of SSL varied across data domains, significant improvement was demonstrated for the most challenging data domain--the blogosphere--when a domain transfer-based SSL strategy was implemented

Multilingual opinion mining

170 p.Cada día se genera gran cantidad de texto en diferentes medios online. Gran parte de ese texto contiene opiniones acerca de multitud de entidades, productos, servicios, etc. Dada la creciente necesidad de disponer de medios automatizados para analizar, procesar y explotar esa información, las técnicas de análisis de sentimiento han recibido gran cantidad de atención por parte de la industria y la comunidad científica durante la última década y media. No obstante, muchas de las técnicas empleadas suelen requerir de entrenamiento supervisado utilizando para ello ejemplos anotados manualmente, u otros recursos lingüísticos relacionados con un idioma o dominio de aplicación específicos. Esto limita la aplicación de este tipo de técnicas, ya que dicho recursos y ejemplos anotados no son sencillos de obtener. En esta tesis se explora una serie de métodos para realizar diversos análisis automáticos de texto en el marco del análisis de sentimiento, incluyendo la obtención automática de términos de un dominio, palabras que expresan opinión, polaridad del sentimiento de dichas palabras (positivas o negativas), etc. Finalmente se propone y se evalúa un método que combina representación continua de palabras (continuous word embeddings) y topic-modelling inspirado en la técnica de Latent Dirichlet Allocation (LDA), para obtener un sistema de análisis de sentimiento basado en aspectos (ABSA), que sólo necesita unas pocas palabras semilla para procesar textos de un idioma o dominio determinados. De este modo, la adaptación a otro idioma o dominio se reduce a la traducción de las palabras semilla correspondientes

Sentiment polarity shifters : creating lexical resources through manual annotation and bootstrapped machine learning

Alleviating pain is good and abandoning hope is bad. We instinctively understand how words like "alleviate" and "abandon" affect the polarity of a phrase, inverting or weakening it. When these words are content words, such as verbs, nouns and adjectives, we refer to them as polarity shifters. Shifters are a frequent occurrence in human language and an important part of successfully modeling negation in sentiment analysis; yet research on negation modeling has focussed almost exclusively on a small handful of closed class negation words, such as "not", "no" and "without. A major reason for this is that shifters are far more lexically diverse than negation words, but no resources exist to help identify them. We seek to remedy this lack of shifter resources. Our most central step towards this is the creation of a large lexicon of polarity shifters that covers verbs, nouns and adjectives. To reduce the prohibitive cost of such a large annotation task, we develop a bootstrapping approach that combines automatic classification with human verification. This ensures the high quality of our lexicon while reducing annotation cost by over 70%. In designing the bootstrap classifier we develop a variety of features which use both existing semantic resources and linguistically informed text patterns. In addition we investigate how knowledge about polarity shifters might be shared across different parts of speech, highlighting both the potential and limitations of such an approach. The applicability of our bootstrapping approach extends beyond the creation of a single resource. We show how it can further be used to introduce polarity shifter resources for other languages. Through the example case of German we show that all our features are transferable to other languages. Keeping in mind the requirements of under-resourced languages, we also explore how well a classifier would do when relying only on data- but not resource-driven features. We also introduce ways to use cross-lingual information, leveraging the shifter resources we previously created for other languages. Apart from the general question of which words can be polarity shifters, we also explore a number of other factors. One of these is the matter of shifting directions, which indicates whether a shifter affects positive polarities, negative polarities or whether it can shift in either direction. Using a supervised classifier we add shifting direction information to our bootstrapped lexicon. For other aspects of polarity shifting, manual annotation is preferable to automatic classification. Not every word that can cause polarity shifting does so for every of its word senses. As word sense disambiguation technology is not robust enough to allow the automatic handling of such nuances, we manually create a complete sense-level annotation of verbal polarity shifters. To verify the usefulness of the lexica which we create, we provide an extrinsic evaluation in which we apply them to a sentiment analysis task. In this task the different lexica are not only compared amongst each other, but also against a state-of-the-art compositional polarity neural network classifier that has been shown to be able to implicitly learn the negating effect of negation words from a training corpus. However, we find that the same is not true for the far more lexically diverse polarity shifters. Instead, the use of the explicit knowledge provided by our shifter lexica brings clear gains in performance.Deutsche Forschungsgesellschaf

Fine-grained Subjectivity and Sentiment Analysis: Recognizing the intensity, polarity, and attitudes of private states

Private states (mental and emotional states) are part of the information that is conveyed in many forms of discourse. News articles often report emotional responses to news stories; editorials, reviews, and weblogs convey opinions and beliefs. This dissertation investigates the manual and automatic identification of linguistic expressions of private states in a corpus of news documents from the world press. A term for the linguistic expression of private states is subjectivity.The conceptual representation of private states used in this dissertation is that of Wiebe et al. (2005). As part of this research, annotators are trained to identify expressions of private states and their properties, such as the source and the intensity of the private state. This dissertation then extends the conceptual representation of private states to better model the attitudes and targets of private states. The inter-annotator agreement studies conducted for this dissertation show that the various concepts in the original and extended representation of private states can be reliably annotated.Exploring the automatic recognition of various types of private states is also a large part of this dissertation. Experiments are conducted that focus on three types of fine-grained subjectivity analysis: recognizing the intensity of clauses and sentences, recognizing the contextual polarity of words and phrases, and recognizing the attribution levels where sentiment and arguing attitudes are expressed. Various supervised machine learning algorithms are used to train automatic systems to perform each of these tasks. These experiments result in automatic systems for performing fine-grained subjectivity analysis that significantly outperform baseline systems

The impact of social participation and attachment styles on mentalizing and emotion regulation in adults living in the United States

Prior studies have shown that attachment styles interact with social inclusion to impact belonging, self-esteem, control, sense of meaning, and positive mood. No studies have investigated how the interaction of attachment and social participation impacts selfregulatory mechanisms. The main goal of this study was to address this gap and investigate how the interaction of different social participation conditions (ostracism, overinclusion, inclusion) and attachment styles impact two regulatory mechanisms, specifically, mentalizing capacities and emotion regulation. Adult participants were recruited through Amazon Mechanical Turk. This was the first study to demonstrate that ostracism and inclusion influence the relationship between attachment style and regulatory mechanisms. Specifically, in the ostracism condition, anxious attachment was associated with greater state emotion regulation difficulties, namely, limited ability to modulate emotional/behavioral responses and lack of emotional clarity. In the inclusion condition, avoidant attachment was associated with lower online mentalizing. The exploratory study demonstrated how different aspects of dispositional mentalizing mediate the relationship between attachment and emotion regulation. Specifically, avoidant and anxious attachment were negatively related to state emotion regulation difficulties, and this was mediated by dispositional uncertainty of mental states. Avoidant attachment was positively related to state emotion regulation difficulties, and this was mediated by a disposition for adequate mentalizing. Finally, results emphasize the importance of improving construct validity in the self-report measure of dispositional mentalizing