InfoExtractor—A Tool for Social Media Data Mining

File, Charles; Shah, Chirag

doi:10.1080/19331681.2012.665259

Journal of Information Technology & Politics

2012

DOI: 10.1080/19331681.2012.665259

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InfoExtractor—A Tool for Social Media Data Mining

Charles File

Chirag Shah

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2016

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Cited by 3 publications

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Web Video Object Mining: Expectation Maximization and Density Based Clustering of Web Video Metadata Objects

Algur¹,

Bhat²

2016

IJIEEB

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Nowadays YouTube becoming most popular video sharing website, and is established in 2005. The YouTube official website is providing different categories videos including Science and Technology, Films and Animation, News and politics, Movies, Comedy, Sports, Music etc. Each video hosted in website such as YouTube have its own identity and features. The identity and features of each video can be described by web video metadata objects such as-URL of each video, category, length of the video, rating information, view counts, comment information, key words etc. Using extracted web video metadata objects, we present an indepth and systematic clustering study on the metadata objects of YouTube videos using Expectation Maximization (EM) and Density Based (DB) clustering approach. Distinct web video metadata object clusters are formed based on different category of web videos. The resultant clusters are analyzed in depth as a step in the KDD process.

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Web Video Object Mining: Expectation Maximization and Density Based Clustering of Web Video Metadata Objects

Algur¹,

Bhat²

2016

IJIEEB

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Web Video Object Mining: A Novel Approach for Knowledge Discovery

Algur¹,

Bhat²

2016

IJISA

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Abstract-The impact of social Medias such as YouTube, Twitter, and FaceBook etc on the modern world is led to huge growth in the size of video data over the cloud and web. The evolution of smart phones/Tabs could be one of the reasons for increasing in the rate of huge video data over the web. Due to the rapid evolution of web videos over the web, it is becoming difficult to identify popular, non-popular and average popular videos without watching the content of it. To cluster web videos based on their metadata into 'Popular', 'Non-Popular', and 'Average Popular' is one of the complex research questions for the Social Media and Computer Science researchers'. In this work, we propose two effective methods to cluster web videos based on their metaobjects. Large scale web video meta-objects such aslength, view counts, numbers of comments, rating information are considered for knowledge discovery process. The two clustering algorithms-Expectation Maximization (EM) and Distribution Based (DB) clustering are used to form three types of clusters. The resultant clusters are analyzed to find popular video cluster, average popular video cluster and non-popular video clusters. And also the results of EM and DB clusters are compared as a step in the process of knowledge discovery.

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Web Video Mining: Metadata Predictive Analysis using Classification Techniques

Algur¹,

Bhat²

2016

IJITCS

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Now a days, the Data Engineering becoming emerging trend to discover knowledge from web audiovisual data such as-YouTube videos, Yahoo Screen, Face Book videos etc. Different categories of web video are being shared on such social websites and are being used by the billions of users all over the world. The uploaded web videos will have different kind of metadata as attribute information of the video data. The metadata attributes defines the contents and features/characteristics of the web videos conceptually. Hence, accomplishing web video mining by extracting features of web videos in terms of metadata is a challenging task. In this work, effective attempts are made to classify and predict the metadata features of web videos such as length of the web videos, number of comments of the web videos, ratings information and view counts of the web videos using data mining algorithms such as Decision tree J48 and navie Bayesian algorithms as a part of web video mining. The results of Decision tree J48 and navie Bayesian classification models are analyzed and compared as a step in the process of knowledge discovery from web videos.

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InfoExtractor—A Tool for Social Media Data Mining

Cited by 3 publications

References 12 publications

Web Video Object Mining: Expectation Maximization and Density Based Clustering of Web Video Metadata Objects

Web Video Object Mining: Expectation Maximization and Density Based Clustering of Web Video Metadata Objects

Web Video Object Mining: A Novel Approach for Knowledge Discovery

Web Video Mining: Metadata Predictive Analysis using Classification Techniques

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