Relatedness-based Multi-Entity Summarization

Representing world knowledge in a machine processable format is important as entities and their descriptions have fueled tremendous growth in knowledge-rich information processing platforms, services, and systems. Prominent applications of knowledge graphs include search engines (e.g., Google Search and Microsoft Bing), email clients (e.g., Gmail), and intelligent personal assistants (e.g., Google Now, Amazon Echo, and Apple’s Siri). In this paper, we present an approach that can summarize facts about a collection of entities by analyzing their relatedness in preference to summarizing each entity in isolation. Specifically, we generate informative entity summaries by selecting: (i) inter-entity facts that are similar and (ii) intra-entity facts that are important and diverse. We employ a constrained knapsack problem solving approach to efficiently compute entity summaries. We perform both qualitative and quantitative experiments and demonstrate that our approach yields promising results compared to two other stand-alone state-of-the-art entity summarization approaches

Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media

With the rise of social media, millions of people are routinely expressing their moods, feelings, and daily struggles with mental health issues on social media platforms like Twitter. Unlike traditional observational cohort studies conducted through questionnaires and self-reported surveys, we explore the reliable detection of clinical depression from tweets obtained unobtrusively. Based on the analysis of tweets crawled from users with self-reported depressive symptoms in their Twitter profiles, we demonstrate the potential for detecting clinical depression symptoms which emulate the PHQ-9 questionnaire clinicians use today. Our study uses a semi-supervised statistical model to evaluate how the duration of these symptoms and their expression on Twitter (in terms of word usage patterns and topical preferences) align with the medical findings reported via the PHQ-9. Our proactive and automatic screening tool is able to identify clinical depressive symptoms with an accuracy of 68% and precision of 72%.Comment: 8 pages, Advances in Social Networks Analysis and Mining (ASONAM), 2017 IEEE/ACM International Conferenc

Multimodal Mental Health Analysis in Social Media

Depression is a major public health concern in the U.S. and globally. While successful early identification and treatment can lead to many positive health and behavioral outcomes, depression, remains undiagnosed, untreated or undertreated due to several reasons, including denial of the illness as well as cultural and social stigma. With the ubiquity of social media platforms, millions of people are now sharing their online persona by expressing their thoughts, moods, emotions, and even their daily struggles with mental health on social media. Unlike traditional observational cohort studies conducted through questionnaires and self-reported surveys, we explore the reliable detection of depressive symptoms from tweets obtained, unobtrusively. Particularly, we examine and exploit multimodal big (social) data to discern depressive behaviors using a wide variety of features including individual-level demographics. By developing a multimodal framework and employing statistical techniques to fuse heterogeneous sets of features obtained through the processing of visual, textual, and user interaction data, we significantly enhance the current state-of-the-art approaches for identifying depressed individuals on Twitter (improving the average F1-Score by 5 percent) as well as facilitate demographic inferences from social media. Besides providing insights into the relationship between demographics and mental health, our research assists in the design of a new breed of demographic-aware health interventions

Fuzzy based implicit sentiment analysis on quantitative sentences

With the rapid growth of social media on the web, emotional polarity computation has become a flourishing frontier in the text mining community. However, it is challenging to understand the latest trends and summarise the state or general opinions about products due to the big diversity and size of social media data and this creates the need of automated and real time opinion extraction and mining. On the other hand, the bulk of currently research has been devoted to study the subjective sentences which contain opinion keyword and limited work has been reported for objective statement that implies sentiment. In this regard, fuzzy based knowledge engineering model has been developed for sentiment classification of special group of such sentences including the change or deviate from desired range or value. Drug reviews are the rich source of such statements. Therefore, in this research, 210 reviews were collected from patient’s review for building corpus. These reviews have been selected from different cholesterol lowering drugs. Medical experts cooperated in this research for building Gold standard corpus. Pre-processing operations including extracting medical terms and their corresponding values have been done on this corpus. An appropriate technique has been developed to map each of these medical terms to their corresponding values. Resulted documents were stored into XML file. Determining sentiment polarity of each sentence employing fuzzy knowledge based system is the next step of this research. The main conclusion through this study is, in order to increase the accuracy level of drug opinion mining system, objective sentences which imply opinion should be taken into consideration

Towards employing social media for studying mental health

Doctor of PhilosophyDepartment of Computer SciencePascal HitzlerWith the ubiquity of social media platforms, millions of people are now sharing their online persona by expressing their thoughts, moods, emotions, and even their daily struggles with mental health on social media. Unlike traditional observational cohort studies conducted through questionnaires and self-reported surveys, we explore the reliable detection of clinical depression from social media obtained unobtrusively. First, we developed a semi-supervised statistical model to evaluate how the duration of these symptoms and their expression on Twitter align with the medical findings reported via the PHQ-9. Based on the analysis of tweets crawled from users, we demonstrate the potential of detecting clinical depression symptoms which emulate the PHQ-9 questionnaire clinicians use today. Over the course of this dissertation, we examine and exploit multi-modal big (social) data to discern depressive behaviors using a wide variety of features including individual-level demographics. By developing a multi-modal framework and employing statistical techniques to fuse heterogeneous sets of features obtained through the processing of visual, textual, and user interaction data, we significantly enhance the current state-of-the-art approaches for identifying depressed individuals on social media as well as facilitate demographic inferences from social media. Besides providing insights into the relationship between demographics and mental health, our research assists in the design of a new breed of demographic-aware health interventions. Altogether, these research topics, resulted in a framework, that when executed, will assist in identifying community-level risk and protective factors associated with the diagnosis and treatment of depression that could be an efficient means of studying patterns of access and utilization of mental health services to inform interventions

Identifying Depressive Disorder in the Twitter Population

Depression is a highly prevalent public health challenge and a major cause of disability across the globe. Annually 6.7% of Americans (that is, more than 16 million). Traditional approaches to curb depression involve survey·based methods via phone or online questionnaires. Large temporal gaps and cognitive bias. Social media provides a method for learning users\u27 feelings, emotions, behaviors, and decisions in real-time

Analyzing Clinical Depressive Symptoms in Twitter

350 million people are suffering from clinical depression worldwide