Faculty of Computinghttps://onlineresource.ucsy.edu.mm/handle/123456789/292024-03-28T21:59:18Z2024-03-28T21:59:18ZTopic Extraction of Crawled Documents Collection using Correlated Topic Model in MapReduce FrameworkOo, Mi KhineKhine, May Ayehttps://onlineresource.ucsy.edu.mm/handle/123456789/26022022-04-24T08:25:21Z2019-12-01T00:00:00ZTopic Extraction of Crawled Documents Collection using Correlated Topic Model in MapReduce Framework
Oo, Mi Khine; Khine, May Aye
The tremendous increase in the amount of available research documents impels researchers to propose
topic models to extract the latent semantic themes of a documents collection. However, how to extract the
hidden topics of the documents collection has become a crucial task for many topic model applications.
Moreover, conventional topic modeling approaches suffer from the scalability problem when the size of
documents collection increases. In this paper, the Correlated Topic Model with variational Expectation Maximization algorithm is implemented in MapReduce framework to solve the scalability problem. The
proposed approach utilizes the dataset crawled from the public digital library. In addition, the full-texts of
the crawled documents are analysed to enhance the accuracy of MapReduce CTM. The experiments are
conducted to demonstrate the performance of the proposed algorithm. From the evaluation, the proposed
approach has a comparable performance in terms of topic coherences with LDA implemented in
MapReduce framework.
Topic Extraction of Crawled Documents Collection using Correlated Topic Model in MapReduce Framework
2019-12-01T00:00:00ZCorrelated Topic Modeling for Big Data with MapReduceOo, Mi KhineKhine, May Ayehttps://onlineresource.ucsy.edu.mm/handle/123456789/26012022-04-24T08:15:21Z2018-10-01T00:00:00ZCorrelated Topic Modeling for Big Data with MapReduce
Oo, Mi Khine; Khine, May Aye
Efficient extraction of useful information is a
rising problem in Big data, since the amount of information
being gathered across various domains grows with an increasing
rate. So, it takes more time to understand the underlying themes
of the documents collection. To deal with such problem in the
context of Big data, the proposed approach implements the
correlated topic model (CTM) with MapReduce framework to
reveal the thematic information represented by words, to speed
up the processing and to increase the scalability of the model.
We apply variational Expectation-Maximization (EM) to make
inference for CTM. In this paper, academic articles are collected
by using a web crawler. Then CTM is exploited to uncover the
underlying themes of the collection. The use of CTM with
MapReduce implementation improves the accuracy and
performance in a reliable and scalable manner.
Correlated Topic Modeling for Big Data with MapReduce
2018-10-01T00:00:00ZScience-related Articles Recommendation System from Big DataOo, Mi KhineKhaing, Myo Kayhttps://onlineresource.ucsy.edu.mm/handle/123456789/26002022-04-24T08:10:21Z2016-02-01T00:00:00ZScience-related Articles Recommendation System from Big Data
Oo, Mi Khine; Khaing, Myo Kay
Under the explosive increase of global data,
the term Big data is mainly used to describe
enormous datasets. With the availability of
increasingly large quantities of digital
information, it is becoming more difficult for
researchers to extract and find relevant articles
pertinent to their interests. In this system, we
propose an approach to discover and
recommend the desired articles by combining
collaborative filtering (CF) with topic modeling.
Correlated Topic Model (CTM) is used for
modeling topics. Our approach not only
considers the interactions between users through
collaborative filtering but also learns the
properties of items involved through topic
modeling to improve recommendation. In order
to handle a large dataset, a Big data analytics
tool Hadoop is used to perform processing over
distributed clusters. The proposed approach
learns the accuracy of the recommendation.
Science-related Articles Recommendation System from Big Data
2016-02-01T00:00:00ZA Study on Community Overlapping Detection Algorithms in Social NetworksWin, Eaint MonKhine, May Ayehttps://onlineresource.ucsy.edu.mm/handle/123456789/25072020-03-17T12:19:55Z2020-02-28T00:00:00ZA Study on Community Overlapping Detection Algorithms in Social Networks
Win, Eaint Mon; Khine, May Aye
Community detection is one of the most important research area wherein invention and growth
of social networks. Community is a set of members densely connected within a group and sparely connected
with the other groups. In social networks, the singular characteristic of communities is multi membership of a
node resulting in overlapping communities. Another relevant feature of social networks is the possibility to
evolve over time. In recent years, many researchers have worked on various methods that can efficiently
unveil overlapped structure on dynamic network. This paper reviews the previous studies done on the
problem of overlapping community detection algorithms. Moreover, some approaches for dynamic network
that change from time to time are also described.
2020-02-28T00:00:00Z