1,866 research outputs found
Examining Accumulated Emotional Traits in Suicide Blogs With an Emotion Topic Model
Suicide has been a major cause of death throughout the world. Recent studies have proved a reliable connection between the emotional traits and suicide. However, detection and prevention of suicide are mostly carried out in the clinical centers, which limits the effective treatments to a restricted group of people. To assist detecting suicide risks among the public, we propose a novel method by exploring the accumulated emotional information from people’s daily writings (i.e. Blogs), and examining these emotional traits which are predictive of suicidal behaviors. A complex emotion topic (CET) model is employed to detect the underlying emotions and emotion-related topics in the Blog streams, based on eight basic emotion categories and five levels of emotion intensities. Since suicide is caused through an accumulative process, we propose three accumulative emotional traits, i.e., accumulation, covariance, and transition of the consecutive Blog emotions, and employ a generalized linear regression algorithm to examine the relationship between emotional traits and suicide risk. Our experiment results suggest that the emotion transition trait turns to be more discriminative of the suicide risk, and that the combination of three traits in linear regression would generate even more discriminative predictions. A classification of the suicide and non-suicide Blog articles in our additional experiment verifies this result. Finally, we conduct a case study of the most commonly mentioned emotion-related topics in the suicidal Blogs, to further understand the association between emotions and thoughts for these authors
Exploring Latent Semantic Information for Textual Emotion Recognition in Blog Articles
Understanding people's emotions through natural language is a challenging task for intelligent systems based on Internet of Things (IoT). The major difficulty is caused by the lack of basic knowledge in emotion expressions with respect to a variety of real world contexts. In this paper, we propose a Bayesian inference method to explore the latent semantic dimensions as contextual information in natural language and to learn the knowledge of emotion expressions based on these semantic dimensions. Our method synchronously infers the latent semantic dimensions as topics in words and predicts the emotion labels in both word-level and document-level texts. The Bayesian inference results enable us to visualize the connection between words and emotions with respect to different semantic dimensions. And by further incorporating a corpus-level hierarchy in the document emotion distribution assumption, we could balance the document emotion recognition results and achieve even better word and document emotion predictions. Our experiment of the word-level and the document-level emotion predictions, based on a well-developed Chinese emotion corpus Ren-CECps, renders both higher accuracy and better robustness in the word-level and the document-level emotion predictions compared to the state-of-the-art emotion prediction algorithms
Supervised learning for suicidal ideation detection in online user content
Copyright © 2018 Shaoxiong Ji et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Early detection and treatment are regarded as the most effective ways to prevent suicidal ideation and potential suicide attempts-two critical risk factors resulting in successful suicides. Online communication channels are becoming a new way for people to express their suicidal tendencies. This paper presents an approach to understand suicidal ideation through online user-generated content with the goal of early detection via supervised learning. Analysing users' language preferences and topic descriptions reveals rich knowledge that can be used as an early warning system for detecting suicidal tendencies. Suicidal individuals express strong negative feelings, anxiety, and hopelessness. Suicidal thoughts may involve family and friends. And topics they discuss cover both personal and social issues. To detect suicidal ideation, we extract several informative sets of features, including statistical, syntactic, linguistic, word embedding, and topic features, and we compare six classifiers, including four traditional supervised classifiers and two neural network models. An experimental study demonstrates the feasibility and practicability of the approach and provides benchmarks for the suicidal ideation detection on the active online platforms: Reddit SuicideWatch and Twitter
Suicidal Ideation and Mental Disorder Detection with Attentive Relation Networks
Mental health is a critical issue in modern society, and mental disorders
could sometimes turn to suicidal ideation without effective treatment. Early
detection of mental disorders and suicidal ideation from social content
provides a potential way for effective social intervention. However,
classifying suicidal ideation and other mental disorders is challenging as they
share similar patterns in language usage and sentimental polarity. This paper
enhances text representation with lexicon-based sentiment scores and latent
topics and proposes using relation networks to detect suicidal ideation and
mental disorders with related risk indicators. The relation module is further
equipped with the attention mechanism to prioritize more critical relational
features. Through experiments on three real-world datasets, our model
outperforms most of its counterparts
Knowledge Modelling and Learning through Cognitive Networks
One of the most promising developments in modelling knowledge is cognitive network science, which aims to investigate cognitive phenomena driven by the networked, associative organization of knowledge. For example, investigating the structure of semantic memory via semantic networks has illuminated how memory recall patterns influence phenomena such as creativity, memory search, learning, and more generally, knowledge acquisition, exploration, and exploitation. In parallel, neural network models for artificial intelligence (AI) are also becoming more widespread as inferential models for understanding which features drive language-related phenomena such as meaning reconstruction, stance detection, and emotional profiling. Whereas cognitive networks map explicitly which entities engage in associative relationships, neural networks perform an implicit mapping of correlations in cognitive data as weights, obtained after training over labelled data and whose interpretation is not immediately evident to the experimenter. This book aims to bring together quantitative, innovative research that focuses on modelling knowledge through cognitive and neural networks to gain insight into mechanisms driving cognitive processes related to knowledge structuring, exploration, and learning. The book comprises a variety of publication types, including reviews and theoretical papers, empirical research, computational modelling, and big data analysis. All papers here share a commonality: they demonstrate how the application of network science and AI can extend and broaden cognitive science in ways that traditional approaches cannot
The Stylometric Processing of Sensory Open Source Data
This research project’s end goal is on the Lone Wolf Terrorist.
The project uses an exploratory approach to the
self-radicalisation problem by creating a stylistic fingerprint
of a person's personality, or self, from subtle characteristics
hidden in a person's writing style. It separates the identity of
one person from another based on their writing style. It also
separates the writings of suicide attackers from ‘normal'
bloggers by critical slowing down; a dynamical property used to
develop early warning signs of tipping points. It identifies
changes in a person's moods, or shifts from one state to another,
that might indicate a tipping point for self-radicalisation.
Research into authorship identity using personality is a
relatively new area in the field of neurolinguistics. There are
very few methods that model how an individual's cognitive
functions present themselves in writing. Here, we develop a
novel algorithm, RPAS, which draws on cognitive functions such as
aging, sensory processing, abstract or concrete thinking through
referential activity emotional experiences, and a person's
internal gender for identity. We use well-known techniques such
as Principal Component Analysis, Linear Discriminant Analysis,
and the Vector Space Method to cluster multiple
anonymous-authored works. Here we use a new approach, using
seriation with noise to separate subtle features in individuals.
We conduct time series analysis using modified variants of 1-lag
autocorrelation and the coefficient of skewness, two statistical
metrics that change near a tipping point, to track serious life
events in an individual through cognitive linguistic markers.
In our journey of discovery, we uncover secrets about the
Elizabethan playwrights hidden for over 400 years. We uncover
markers for depression and anxiety in modern-day writers and
identify linguistic cues for Alzheimer's disease much earlier
than other studies using sensory processing. In using these
techniques on the Lone Wolf, we can separate their writing style
used before their attacks that differs from other writing
Direct-to-Consumer Genetic Testing on Social Media: Topic Modeling and Sentiment Analysis of YouTube Users\u27 Comments
Background:
With direct-to-consumer (DTC) genetic testing enabling self-responsible access to novel information on ancestry, traits, or health, consumers often turn to social media for assistance and discussion. YouTube, the largest social media platform for videos, offers an abundance of DTC genetic testing–related videos. Nevertheless, user discourse in the comments sections of these videos is largely unexplored.
Objective:
This study aims to address the lack of knowledge concerning user discourse in the comments sections of DTC genetic testing–related videos on YouTube by exploring topics discussed and users\u27 attitudes toward these videos.
Methods:
We employed a 3-step research approach. First, we collected metadata and comments of the 248 most viewed DTC genetic testing–related videos on YouTube. Second, we conducted topic modeling using word frequency analysis, bigram analysis, and structural topic modeling to identify topics discussed in the comments sections of those videos. Finally, we employed Bing (binary), National Research Council Canada (NRC) emotion, and 9-level sentiment analysis to identify users\u27 attitudes toward these DTC genetic testing–related videos, as expressed in their comments.
Results:
We collected 84,082 comments from the 248 most viewed DTC genetic testing–related YouTube videos. With topic modeling, we identified 6 prevailing topics on (1) general genetic testing, (2) ancestry testing, (3) relationship testing, (4) health and trait testing, (5) ethical concerns, and (6) YouTube video reaction. Further, our sentiment analysis indicates strong positive emotions (anticipation, joy, surprise, and trust) and a neutral-to-positive attitude toward DTC genetic testing–related videos.
Conclusions:
With this study, we demonstrate how to identify users\u27 attitudes on DTC genetic testing by examining topics and opinions based on YouTube video comments. Shedding light on user discourse on social media, our findings suggest that users are highly interested in DTC genetic testing and related social media content. Nonetheless, with this novel market constantly evolving, service providers, content providers, or regulatory authorities may still need to adapt their services to users\u27 interests and desires
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