Learning domain-specific sentiment lexicons with applications to recommender systems

Search is now going beyond looking for factual information, and people wish to search for the opinions of others to help them in their own decision-making. Sentiment expressions or opinion expressions are used by users to express their opinion and embody important pieces of information, particularly in online commerce. The main problem that the present dissertation addresses is how to model text to find meaningful words that express a sentiment. In this context, I investigate the viability of automatically generating a sentiment lexicon for opinion retrieval and sentiment classification applications. For this research objective we propose to capture sentiment words that are derived from online users’ reviews. In this approach, we tackle a major challenge in sentiment analysis which is the detection of words that express subjective preference and domain-specific sentiment words such as jargon. To this aim we present a fully generative method that automatically learns a domain-specific lexicon and is fully independent of external sources. Sentiment lexicons can be applied in a broad set of applications, however popular recommendation algorithms have somehow been disconnected from sentiment analysis. Therefore, we present a study that explores the viability of applying sentiment analysis techniques to infer ratings in a recommendation algorithm. Furthermore, entities’ reputation is intrinsically associated with sentiment words that have a positive or negative relation with those entities. Hence, is provided a study that observes the viability of using a domain-specific lexicon to compute entities reputation. Finally, a recommendation system algorithm is improved with the use of sentiment-based ratings and entities reputation

Describing Images by Semantic Modeling using Attributes and Tags

This dissertation addresses the problem of describing images using visual attributes and textual tags, a fundamental task that narrows down the semantic gap between the visual reasoning of humans and machines. Automatic image annotation assigns relevant textual tags to the images. In this dissertation, we propose a query-specific formulation based on Weighted Multi-view Non-negative Matrix Factorization to perform automatic image annotation. Our proposed technique seamlessly adapt to the changes in training data, naturally solves the problem of feature fusion and handles the challenge of the rare tags. Unlike tags, attributes are category-agnostic, hence their combination models an exponential number of semantic labels. Motivated by the fact that most attributes describe local properties, we propose exploiting localization cues, through semantic parsing of human face and body to improve person-related attribute prediction. We also demonstrate that image-level attribute labels can be effectively used as weak supervision for the task of semantic segmentation. Next, we analyze the Selfie images by utilizing tags and attributes. We collect the first large-scale Selfie dataset and annotate it with different attributes covering characteristics such as gender, age, race, facial gestures, and hairstyle. We then study the popularity and sentiments of the selfies given an estimated appearance of various semantic concepts. In brief, we automatically infer what makes a good selfie. Despite its extensive usage, the deep learning literature falls short in understanding the characteristics and behavior of the Batch Normalization. We conclude this dissertation by providing a fresh view, in light of information geometry and Fisher kernels to why the batch normalization works. We propose Mixture Normalization that disentangles modes of variation in the underlying distribution of the layer outputs and confirm that it effectively accelerates training of different batch-normalized architectures including Inception-V3, Densely Connected Networks, and Deep Convolutional Generative Adversarial Networks while achieving better generalization error

Multi-layered HITS on Multi-sourced Networks

abstract: Network mining has been attracting a lot of research attention because of the prevalence of networks. As the world is becoming increasingly connected and correlated, networks arising from inter-dependent application domains are often collected from different sources, forming the so-called multi-sourced networks. Examples of such multi-sourced networks include critical infrastructure networks, multi-platform social networks, cross-domain collaboration networks, and many more. Compared with single-sourced network, multi-sourced networks bear more complex structures and therefore could potentially contain more valuable information. This thesis proposes a multi-layered HITS (Hyperlink-Induced Topic Search) algorithm to perform the ranking task on multi-sourced networks. Specifically, each node in the network receives an authority score and a hub score for evaluating the value of the node itself and the value of its outgoing links respectively. Based on a recent multi-layered network model, which allows more flexible dependency structure across different sources (i.e., layers), the proposed algorithm leverages both within-layer smoothness and cross-layer consistency. This essentially allows nodes from different layers to be ranked accordingly. The multi-layered HITS is formulated as a regularized optimization problem with non-negative constraint and solved by an iterative update process. Extensive experimental evaluations demonstrate the effectiveness and explainability of the proposed algorithm.Dissertation/ThesisMasters Thesis Computer Science 201

A Survey on Knowledge Graphs: Representation, Acquisition and Applications

Human knowledge provides a formal understanding of the world. Knowledge graphs that represent structural relations between entities have become an increasingly popular research direction towards cognition and human-level intelligence. In this survey, we provide a comprehensive review of knowledge graph covering overall research topics about 1) knowledge graph representation learning, 2) knowledge acquisition and completion, 3) temporal knowledge graph, and 4) knowledge-aware applications, and summarize recent breakthroughs and perspective directions to facilitate future research. We propose a full-view categorization and new taxonomies on these topics. Knowledge graph embedding is organized from four aspects of representation space, scoring function, encoding models, and auxiliary information. For knowledge acquisition, especially knowledge graph completion, embedding methods, path inference, and logical rule reasoning, are reviewed. We further explore several emerging topics, including meta relational learning, commonsense reasoning, and temporal knowledge graphs. To facilitate future research on knowledge graphs, we also provide a curated collection of datasets and open-source libraries on different tasks. In the end, we have a thorough outlook on several promising research directions

Transductive Distributional Correspondence Indexing for Cross-Domain Topic Classification

Abstract. Obtaining high-quality annotated data for training a classifier for a new domain is often costly. Domain Adaptation (DA) aims at leveraging the annotated data available from a different but related source domain in order to deploy a classification model for the target domain of interest, thus alleviating the aforementioned costs. To that aim, the learning model is typically given access to a set of unlabelled documents collected from the target domain. These documents might consist of a representative sample of the target distribution, and they could thus be used to infer a general classification model for the domain (inductive inference). Alternatively, these documents could be the entire set of documents to be classified; this happens when there is only one set of documents we are interested in classifying (transductive inference). Many of the DA methods proposed so far have focused on transductive classification by topic, i.e., the task of assigning class labels to a specific set of documents based on the topics they are about. In this work, we report on new experiments we have conducted in transductive classification by topic using Distributional Correspondence Indexing method, a DA method we have recently developed that delivered state-of-the-art results in inductive classification by sentiment. The results we have obtained on three popular datasets show DCI to be competitive with the state of the art also in this scenario, and to be superior to all compared methods in many cases

Multi-view Representation Learning for Unifying Languages, Knowledge and Vision

The growth of content on the web has raised various challenges, yet also provided numerous opportunities. Content exists in varied forms such as text appearing in different languages, entity-relationship graph represented as structured knowledge and as a visual embodiment like images/videos. They are often referred to as modalities. In many instances, the different amalgamation of modalities co-exists to complement each other or to provide consensus. Thus making the content either heterogeneous or homogeneous. Having an additional point of view for each instance in the content is beneficial for data-driven learning and intelligent content processing. However, despite having availability of such content. Most advancements made in data-driven learning (i.e., machine learning) is by solving tasks separately for the single modality. The similar endeavor was not shown for the challenges which required input either from all or subset of them. In this dissertation, we develop models and techniques that can leverage multiple views of heterogeneous or homogeneous content and build a shared representation for aiding several applications which require a combination of modalities mentioned above. In particular, we aim to address applications such as content-based search, categorization, and generation by providing several novel contributions. First, we develop models for heterogeneous content by jointly modeling diverse representations emerging from two views depicting text and image by learning their correlation. To be specific, modeling such correlation is helpful to retrieve cross-modal content. Second, we replace the heterogeneous content with homogeneous to learn a common space representation for content categorization across languages. Furthermore, we develop models that take input from both homogeneous and heterogeneous content to facilitate the construction of common space representation from more than two views. Specifically, representation is used to generate one view from another. Lastly, we describe a model that can handle missing views, and demonstrate that the model can generate missing views by utilizing external knowledge. We argue that techniques the models leverage internally provide many practical benefits and lot of immediate value applications. From the modeling perspective, our contributed model design in this thesis can be summarized under the phrase Multi-view Representation Learning( MVRL ). These models are variations and extensions of shallow statistical and deep neural networks approaches that can jointly optimize and exploit all views of the input content arising from different independent representations. We show that our models advance state of the art, but not limited to tasks such as cross-modal retrieval, cross-language text classification, image-caption generation in multiple languages and caption generation for images containing unseen visual object categories

Foundations and Recent Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions

Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design computer agents with intelligent capabilities such as understanding, reasoning, and learning through integrating multiple communicative modalities, including linguistic, acoustic, visual, tactile, and physiological messages. With the recent interest in video understanding, embodied autonomous agents, text-to-image generation, and multisensor fusion in application domains such as healthcare and robotics, multimodal machine learning has brought unique computational and theoretical challenges to the machine learning community given the heterogeneity of data sources and the interconnections often found between modalities. However, the breadth of progress in multimodal research has made it difficult to identify the common themes and open questions in the field. By synthesizing a broad range of application domains and theoretical frameworks from both historical and recent perspectives, this paper is designed to provide an overview of the computational and theoretical foundations of multimodal machine learning. We start by defining two key principles of modality heterogeneity and interconnections that have driven subsequent innovations, and propose a taxonomy of 6 core technical challenges: representation, alignment, reasoning, generation, transference, and quantification covering historical and recent trends. Recent technical achievements will be presented through the lens of this taxonomy, allowing researchers to understand the similarities and differences across new approaches. We end by motivating several open problems for future research as identified by our taxonomy