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OBOME - Ontology based opinion mining in UBIPOL
Ontologies have a special role in the UBIPOL system, they help to structure the policy related context, provide conceptualization for policy domain and use in the opinion mining process. In this work we presented a system called Ontology Based Opinion Mining Engine (OBOME) for analyzing a domain-specific opinion corpus by first assisting the user with the creation of a domain ontology from the corpus. We determined the polarity of opinion on the various domain aspects. In the former step, the policy domain aspect has are identified (namely which policy category is represented by the concept). This identification is supported by the policy modelling ontology, which describe the most important policy – related classes and structure. Then the most informative documents from the corpus are extracted and asked the user to create a set of aspects and related keywords using these documents. In the latter step, we used the corpus specific ontology to model the domain and extracted aspect-polarity associations using grammatical dependencies between words. Later, summarized results are shown to the user to analyze and store. Finally, in an offline process policy modeling ontology is updated
Deep Memory Networks for Attitude Identification
We consider the task of identifying attitudes towards a given set of entities
from text. Conventionally, this task is decomposed into two separate subtasks:
target detection that identifies whether each entity is mentioned in the text,
either explicitly or implicitly, and polarity classification that classifies
the exact sentiment towards an identified entity (the target) into positive,
negative, or neutral.
Instead, we show that attitude identification can be solved with an
end-to-end machine learning architecture, in which the two subtasks are
interleaved by a deep memory network. In this way, signals produced in target
detection provide clues for polarity classification, and reversely, the
predicted polarity provides feedback to the identification of targets.
Moreover, the treatments for the set of targets also influence each other --
the learned representations may share the same semantics for some targets but
vary for others. The proposed deep memory network, the AttNet, outperforms
methods that do not consider the interactions between the subtasks or those
among the targets, including conventional machine learning methods and the
state-of-the-art deep learning models.Comment: Accepted to WSDM'1
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
Lifelong Learning CRF for Supervised Aspect Extraction
This paper makes a focused contribution to supervised aspect extraction. It
shows that if the system has performed aspect extraction from many past domains
and retained their results as knowledge, Conditional Random Fields (CRF) can
leverage this knowledge in a lifelong learning manner to extract in a new
domain markedly better than the traditional CRF without using this prior
knowledge. The key innovation is that even after CRF training, the model can
still improve its extraction with experiences in its applications.Comment: Accepted at ACL 2017. arXiv admin note: text overlap with
arXiv:1612.0794
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