Gaining Insight into Determinants of Physical Activity using Bayesian Network Learning

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Model-based feature construction and text representation for social media analysis

Text representation is at the foundation of most text-based applications. Surface features are insufficient for many tasks and therefore constructing powerful discriminative features in a general way is an open challenge. Current approaches use deep neural networks to bypass feature construction. While deep learning can learn sophisticated representations from the text, it requires a lot of training data, which might not be readily available, and the derived features are not necessarily interpretable. In this work, we explore a novel paradigm, model-based feature construction (MBFC), that allows us to construct semantic features that can potentially improve many applications. In brief, MBFC uses human knowledge and expertise as well as big data to guide the design of models that enhance predictive modeling and support the data mining process by extracting useful knowledge, which in turn can be used as features for downstream prediction tasks. In this dissertation, we show how this paradigm can be applied to several tasks of social media analysis. We explore how MBFC can be used to solve the problem of target misalignment for prediction, where the output variable and the data may be at different levels of resolution and the goal is to construct features that can bridge this gap. The MBFC method allows us to use additional related data, e.g. associated context, to facilitate semantic analysis and feature construction. In this dissertation, we focus on a subset of problems in which social media data, in particular text data, can be leveraged to construct useful representations for prediction. We explore several kinds of user-generated content in social media data such as review data for useful review prediction, micro-blogging data for urgent health-based prediction tasks, and discussion forum data for expert prediction. First, we propose a background mixture model to capture incongruity features in text and use these features for humor detection in restaurant reviews. Second, we propose a source reliability feature representation method for trustworthy comment identification that incorporates user aspect expertise when modeling fine-grained reliabilities in an online discussion forum. And finally, we propose multi-view attribute features that adapt MBFC to handle the target misalignment problem for topic-based features and apply this to tweets in order to forecast new diagnosis rates for sexually transmitted infections

Confluence of Vision and Natural Language Processing for Cross-media Semantic Relations Extraction

In this dissertation, we focus on extracting and understanding semantically meaningful relationships between data items of various modalities; especially relations between images and natural language. We explore the ideas and techniques to integrate such cross-media semantic relations for machine understanding of large heterogeneous datasets, made available through the expansion of the World Wide Web. The datasets collected from social media websites, news media outlets and blogging platforms usually contain multiple modalities of data. Intelligent systems are needed to automatically make sense out of these datasets and present them in such a way that humans can find the relevant pieces of information or get a summary of the available material. Such systems have to process multiple modalities of data such as images, text, linguistic features, and structured data in reference to each other. For example, image and video search and retrieval engines are required to understand the relations between visual and textual data so that they can provide relevant answers in the form of images and videos to the users\u27 queries presented in the form of text. We emphasize the automatic extraction of semantic topics or concepts from the data available in any form such as images, free-flowing text or metadata. These semantic concepts/topics become the basis of semantic relations across heterogeneous data types, e.g., visual and textual data. A classic problem involving image-text relations is the automatic generation of textual descriptions of images. This problem is the main focus of our work. In many cases, large amount of text is associated with images. Deep exploration of linguistic features of such text is required to fully utilize the semantic information encoded in it. A news dataset involving images and news articles is an example of this scenario. We devise frameworks for automatic news image description generation based on the semantic relations of images, as well as semantic understanding of linguistic features of the news articles

Learning Context-sensitive Human Emotions in Categorical and Dimensional Domains

Still image emotion recognition (ER) has been receiving increasing attention in recent years due to the tremendous amount of social media content on the Web. Many works offer both categorical and dimensional methods to detect image sentiments, while others focus on extracting the true social signals, such as happiness and anger. Deep learning architectures have delivered great suc- cess, however, their dependency on large-scale datasets labeled with (1) emotion, and (2) valence, arousal and dominance, in categorical and dimensional domains respectively, introduce challenges the community tries to tackle. Emotions offer dissimilar semantics when aroused in different con- texts, however context-sensitive ER has been by and large discarded in the literature so far. Moreover, while dimensional methods deliver higher accuracy, they have been less attended due to (1) lack of reliable large-scale labeled datasets, and (2) challenges involved in architecting un- supervised solutions to the problem. Owing to the success offered by multi-modal ER, still image ER in the single-modal domain; i.e. using only still images, remains less resorted to. In this work, (1) we first architect a novel fully automated dataset collection pipeline, equipped with a built-in semantic sanitizer, (2) we then build UCF-ER with 50K images, and LUCFER, the largest labeled ER dataset in the literature with more than 3.6M images, both datasets labeled with emotion and context, (3) next, we build a single-modal context-sensitive ER CNN model, fine-tuned on UCF-ER and LUCFER, (4) we then claim and show empirically that infusing context to the unified training process helps achieve a more balanced precision and recall, while boosting performance, yielding an overall classification accuracy of 73.12% compared to the state of the art 58.3%, (5) next, we propose an unsupervised approach for ranking of continuous emotions in images using canonical polyadic (CP) decomposition, providing theoretical proof that rank-1 CP decomposition can be used as a ranking machine, (6) finally, we provide empirical proof that our method generates a Pearson Correlation Coefficient, outperforming the state of the art by a large margin; i.e. 65.13% (difference) in one experiment and 104.08% (difference) in another, when applied to valence rank estimation

Low-Resource Unsupervised NMT:Diagnosing the Problem and Providing a Linguistically Motivated Solution

Unsupervised Machine Translation hasbeen advancing our ability to translatewithout parallel data, but state-of-the-artmethods assume an abundance of mono-lingual data. This paper investigates thescenario where monolingual data is lim-ited as well, finding that current unsuper-vised methods suffer in performance un-der this stricter setting. We find that theperformance loss originates from the poorquality of the pretrained monolingual em-beddings, and we propose using linguis-tic information in the embedding train-ing scheme. To support this, we look attwo linguistic features that may help im-prove alignment quality: dependency in-formation and sub-word information. Us-ing dependency-based embeddings resultsin a complementary word representationwhich offers a boost in performance ofaround 1.5 BLEU points compared to stan-dardWORD2VECwhen monolingual datais limited to 1 million sentences per lan-guage. We also find that the inclusion ofsub-word information is crucial to improv-ing the quality of the embedding

Learning Representations of Social Media Users

User representations are routinely used in recommendation systems by platform developers, targeted advertisements by marketers, and by public policy researchers to gauge public opinion across demographic groups. Computer scientists consider the problem of inferring user representations more abstractly; how does one extract a stable user representation - effective for many downstream tasks - from a medium as noisy and complicated as social media? The quality of a user representation is ultimately task-dependent (e.g. does it improve classifier performance, make more accurate recommendations in a recommendation system) but there are proxies that are less sensitive to the specific task. Is the representation predictive of latent properties such as a person's demographic features, socioeconomic class, or mental health state? Is it predictive of the user's future behavior? In this thesis, we begin by showing how user representations can be learned from multiple types of user behavior on social media. We apply several extensions of generalized canonical correlation analysis to learn these representations and evaluate them at three tasks: predicting future hashtag mentions, friending behavior, and demographic features. We then show how user features can be employed as distant supervision to improve topic model fit. Finally, we show how user features can be integrated into and improve existing classifiers in the multitask learning framework. We treat user representations - ground truth gender and mental health features - as auxiliary tasks to improve mental health state prediction. We also use distributed user representations learned in the first chapter to improve tweet-level stance classifiers, showing that distant user information can inform classification tasks at the granularity of a single message.Comment: PhD thesi

Learning Representations of Social Media Users

User representations are routinely used in recommendation systems by platform developers, targeted advertisements by marketers, and by public policy researchers to gauge public opinion across demographic groups. Computer scientists consider the problem of inferring user representations more abstractly; how does one extract a stable user representation - effective for many downstream tasks - from a medium as noisy and complicated as social media? The quality of a user representation is ultimately task-dependent (e.g. does it improve classifier performance, make more accurate recommendations in a recommendation system) but there are proxies that are less sensitive to the specific task. Is the representation predictive of latent properties such as a person's demographic features, socioeconomic class, or mental health state? Is it predictive of the user's future behavior? In this thesis, we begin by showing how user representations can be learned from multiple types of user behavior on social media. We apply several extensions of generalized canonical correlation analysis to learn these representations and evaluate them at three tasks: predicting future hashtag mentions, friending behavior, and demographic features. We then show how user features can be employed as distant supervision to improve topic model fit. Finally, we show how user features can be integrated into and improve existing classifiers in the multitask learning framework. We treat user representations - ground truth gender and mental health features - as auxiliary tasks to improve mental health state prediction. We also use distributed user representations learned in the first chapter to improve tweet-level stance classifiers, showing that distant user information can inform classification tasks at the granularity of a single message.Comment: PhD thesi