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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 â 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
Mining online diaries for blogger identification
In this paper, we present an investigation of authorship
identification on personal blogs or diaries, which are different from other types of text such as essays, emails, or articles based on the text properties. The investigation utilizes couple of intuitive feature sets and studies various parameters that affect the identification performance.
Many studies manipulated the problem of authorship
identification in manually collected corpora, but only few
utilized real data from existing blogs. The complexity of
the language model in personal blogs is motivating to
identify the correspondent author. The main contribution
of this work is at least three folds. Firstly, we utilize the LIWC and MRC feature sets together, which have been
developed with Psychology background, for the first time
for authorship identification on personal blogs. Secondly, we analyze the effect of various parameters, and feature sets, on the identification performance. This includes the number of authors in the data corpus, the post size or the word count, and the number of posts for each author.
Finally, we study applying authorship identification over a limited set of users that have a common personality attributes. This analysis is motivated by the lack of standard or solid recommendations in literature for such task, especially in the domain of personal blogs.
The results and evaluation show that the utilized features
are compact while their performance is highly comparable
with other larger feature sets. The analysis also confirmed
the most effective parameters, their ranges in the data
corpus, and the usefulness of the common users classifier
in improving the performance, for the author identification
task
Identifying Relationships Among Sentences in Court Case Transcripts Using Discourse Relations
Case Law has a significant impact on the proceedings of legal cases.
Therefore, the information that can be obtained from previous court cases is
valuable to lawyers and other legal officials when performing their duties.
This paper describes a methodology of applying discourse relations between
sentences when processing text documents related to the legal domain. In this
study, we developed a mechanism to classify the relationships that can be
observed among sentences in transcripts of United States court cases. First, we
defined relationship types that can be observed between sentences in court case
transcripts. Then we classified pairs of sentences according to the
relationship type by combining a machine learning model and a rule-based
approach. The results obtained through our system were evaluated using human
judges. To the best of our knowledge, this is the first study where discourse
relationships between sentences have been used to determine relationships among
sentences in legal court case transcripts.Comment: Conference: 2018 International Conference on Advances in ICT for
Emerging Regions (ICTer
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