Matching Items (2)
Filtering by
- Creators: Baral, Chitta
- Creators: Hsiao, Sharon
Description
Due to vast resources brought by social media services, social data mining has
received increasing attention in recent years. The availability of sheer amounts of
user-generated data presents data scientists both opportunities and challenges. Opportunities are presented with additional data sources. The abundant link information
in social networks could provide another rich source in deriving implicit information
for social data mining. However, the vast majority of existing studies overwhelmingly
focus on positive links between users while negative links are also prevailing in real-
world social networks such as distrust relations in Epinions and foe links in Slashdot.
Though recent studies show that negative links have some added value over positive
links, it is dicult to directly employ them because of its distinct characteristics from
positive interactions. Another challenge is that label information is rather limited
in social media as the labeling process requires human attention and may be very
expensive. Hence, alternative criteria are needed to guide the learning process for
many tasks such as feature selection and sentiment analysis.
To address above-mentioned issues, I study two novel problems for signed social
networks mining, (1) unsupervised feature selection in signed social networks; and
(2) unsupervised sentiment analysis with signed social networks. To tackle the first problem, I propose a novel unsupervised feature selection framework SignedFS. In
particular, I model positive and negative links simultaneously for user preference
learning, and then embed the user preference learning into feature selection. To study the second problem, I incorporate explicit sentiment signals in textual terms and
implicit sentiment signals from signed social networks into a coherent model Signed-
Senti. Empirical experiments on real-world datasets corroborate the effectiveness of
these two frameworks on the tasks of feature selection and sentiment analysis.
received increasing attention in recent years. The availability of sheer amounts of
user-generated data presents data scientists both opportunities and challenges. Opportunities are presented with additional data sources. The abundant link information
in social networks could provide another rich source in deriving implicit information
for social data mining. However, the vast majority of existing studies overwhelmingly
focus on positive links between users while negative links are also prevailing in real-
world social networks such as distrust relations in Epinions and foe links in Slashdot.
Though recent studies show that negative links have some added value over positive
links, it is dicult to directly employ them because of its distinct characteristics from
positive interactions. Another challenge is that label information is rather limited
in social media as the labeling process requires human attention and may be very
expensive. Hence, alternative criteria are needed to guide the learning process for
many tasks such as feature selection and sentiment analysis.
To address above-mentioned issues, I study two novel problems for signed social
networks mining, (1) unsupervised feature selection in signed social networks; and
(2) unsupervised sentiment analysis with signed social networks. To tackle the first problem, I propose a novel unsupervised feature selection framework SignedFS. In
particular, I model positive and negative links simultaneously for user preference
learning, and then embed the user preference learning into feature selection. To study the second problem, I incorporate explicit sentiment signals in textual terms and
implicit sentiment signals from signed social networks into a coherent model Signed-
Senti. Empirical experiments on real-world datasets corroborate the effectiveness of
these two frameworks on the tasks of feature selection and sentiment analysis.
ContributorsCheng, Kewei (Author) / Liu, Huan (Thesis advisor) / Tong, Hanghang (Committee member) / Baral, Chitta (Committee member) / Arizona State University (Publisher)
Created2017
Description
An ontology is a vocabulary that provides a formal description of entities within a domain and their relationships with other entities. Along with basic schema information, it also captures information in the form of metadata about cardinality, restrictions, hierarchy, and semantic meaning. With the rapid growth of semantic (RDF) data on the web, many organizations like DBpedia, Earth Science Information Partners (ESIP) are publishing more and more data in RDF format. The ontology alignment task aims at linking two or more different ontologies from the same domain or different domains. It is a process of finding the semantic relationship between two or more ontological entities and/or instances. Information/data sharing among different systems is quite limited because of differences in data based on syntax, structures, and semantics. Ontology alignment is used to overcome the limitation of semantic interoperability of current vast distributed systems available on the Web. In spite of the availability of large hierarchical domain-specific datasets, automated ontology mapping is still a complex problem. Over the years, many techniques have been proposed for ontology instance alignment, schema alignment, and link discovery. Most of the available approaches require human intervention or work within a specific domain. The challenge involves representing an entity as a vector that encodes all context information of the entity such as hierarchical information, properties, constraints, etc. The ontological representation is rich in comparison with the regular data schema because of metadata about various properties, constraints, relationship to other entities within the domain, etc. While finding similarities between entities this metadata is often overlooked. The second challenge is that the comparison of two ontologies is an intense operation and highly depends on the domain and the language that the ontologies are expressed in. Most current methods require human intervention that leads to a time-consuming and cumbersome process and the output is prone to human errors. The proposed unsupervised recursive neural network technique achieves an F-measure of 80.3% on the Anatomy dataset and the proposed graph neural network technique achieves an F-measure of 81.0% on the Anatomy dataset.
ContributorsChakraborty, Jaydeep (Author) / Bansal, Srividya (Thesis advisor) / Sherif, Mohamed (Committee member) / Bansal, Ajay (Committee member) / Hsiao, Sharon (Committee member) / Arizona State University (Publisher)
Created2021