A5028828916- Sentiment Analysis and Opinion Mining
- Mental Health via Writing
- Digital Mental Health Interventions
- Mental Health Research Topics
- Advanced Text Analysis Techniques
- Spam and Phishing Detection
- Computational Drug Discovery Methods
- Impact of Technology on Adolescents
- Hate Speech and Cyberbullying Detection
- Data Quality and Management
- Advanced Fiber Laser Technologies
- Advanced Photonic Communication Systems
- Optical Network Technologies
- Drilling and Well Engineering
- Carbon Nanotubes in Composites
- Electrochemical Analysis and Applications
- Pharmacovigilance and Adverse Drug Reactions
- Digital Imaging in Medicine
- Rock Mechanics and Modeling
- Gas Sensing Nanomaterials and Sensors
- Advanced Malware Detection Techniques
- Tunneling and Rock Mechanics
- Biomedical Text Mining and Ontologies
- Complex Network Analysis Techniques
- Media, Religion, Digital Communication
Cornell University
2020
Kansas State University
2020
Wright State University
2016-2018
University of Technology Malaysia
2014-2016
With the rise of social media, millions people are routinely expressing their moods, feelings, and daily struggles with mental health issues on media platforms like Twitter. Unlike traditional observational cohort studies conducted through questionnaires self-reported surveys, we explore reliable detection clinical depression from tweets obtained unobtrusively. Based analysis crawled users depressive symptoms in Twitter profiles, demonstrate potential for detecting which emulate PHQ-9...
Efforts to assess people's sentiments on Twitter have suggested that could be a valuable resource for studying political sentiment and it reflects the offline landscape. Many opinion mining systems tools provide users with attitudes toward products, people, or topics their attributes/aspects. However, although may appear simple, using analysis predict election results is difficult, since empirically challenging train successful model conduct tweet streams dynamic event such as an election....
Depression is a major public health concern in the U.S. and globally. While successful early identification treatment can lead to many positive behavioral outcomes, depression, remains undiagnosed, untreated or undertreated due several reasons, including denial of illness as well cultural social stigma. With ubiquity media platforms, millions people are now sharing their online persona by expressing thoughts, moods, emotions, even daily struggles with mental on media. Unlike traditional...
Microring resonators (MRRs) can be used to generate optical millimetre-wave solitons with a broadband frequency of 40–60 GHz. Non-linear light behaviours within MRRs, such as chaotic signals, logic codes (digital codes). The soliton signals multiplexed and modulated the using an orthogonal division multiplexing (OFDM) technique transmit data via network system. OFDM uses overlapping subcarriers without causing inter-carrier interference. It provides both high rate symbol duration over...
With ubiquity of social media platforms, millions people are routinely sharing their moods, feelings and even daily struggles with mental health issues by expressing it verbally or indirectly through images they post. In this study, we aim to examine exploitation big multi-modal data for studying depressive behavior its population trend across the U.S. better understand a regions influence on prevailing environment available care. partic-ular, employing statistical techniques along fusion...
Purpose Many opinion-mining systems and tools have been developed to provide users with the attitudes of people toward entities their attributes or overall polarities documents. In addition, side effects are one critical measures used evaluate a patient’s opinion for particular drug. However, effect recognition is challenging task, since coincide disease symptoms lexically syntactically. The purpose this paper extract drug from reviews as an integral implicit-opinion words....
Representing world knowledge in a machine processable format is important as entities and their descriptions have fueled tremendous growth knowledge-rich information processing platforms, services, systems. Prominent applications of graphs include search engines (e.g., Google Search Microsoft Bing), email clients Gmail), intelligent personal assistants Now, Amazon Echo, Apple's Siri). In this paper, we present an approach that can summarize facts about collection by analyzing relatedness...
With the proliferation of social media over last decade, determining people's attitude with respect to a specific topic, document, interaction or events has fueled research interest in natural language processing and introduced new channel called sentiment emotion analysis. For instance, businesses routinely look develop systems automatically understand their customer conversations by identifying relevant content enhance marketing products managing reputations. Previous efforts assess on...
With the rapid growth of social media on web, emotional polarity computation has become a flourishing frontier in text mining community. However, it is challenging to understand latest trends and summarize state or general opinions about products due big diversity size data this creates need automated real time opinion extraction mining. On other hand, bulk current research been devoted study subjective sentences which contain keywords limited work reported for objective statements that...
The enormous growth of online reviews in social media provides a valuable resource for human decision-making activities diverse domains such as the medical domain. Extracting explicit and implicit opinions is one main tasks opinion mining area. As complicated task, limited work has been done on it, especially domain, domain dependent task. Side effects are critical concepts recognition which challenging task since it coincides with disease symptoms both lexically syntactically. To best our...
With the rise of social media, millions people are routinely expressing their moods, feelings, and daily struggles with mental health issues on media platforms like Twitter. Unlike traditional observational cohort studies conducted through questionnaires self-reported surveys, we explore reliable detection clinical depression from tweets obtained unobtrusively. Based analysis crawled users depressive symptoms in Twitter profiles, demonstrate potential for detecting which emulate PHQ-9...
With ubiquity of social media platforms, millions people are sharing their online persona by expressing thoughts, moods, emotions, feelings, and even daily struggles with mental health issues voluntarily publicly on media. Unlike the most existing efforts which study depression analyzing textual content, we examine exploit multimodal big data to discern depressive behavior using a wide variety features including individual-level demographics. By developing framework employing statistical...