ISIT219 Knowledge and Information Engineering
[ad_1]
School of Computing and Information Technology
ISIT219
Knowledge and Information Engineering
Assignment 2
Group members: minimum 3, maximum 5
Total mark: 40
Contribution to the final mark: 40%
Submissions: soft copy via Moodle
• report in MS Word or Pdf format (maximum 2500 words)
• submission time: 28 May at 9:00 am
• source code files (such as the RapidMiner process or any other preferred programming
languages)
Business Case
YouTube is one of the largest video-sharing websites worldwide, with an estimated monthly
viewership of 1 billion and serves as an important source for analyzing online user activity. In this
assignment, we are taking YouTube as the main resource. There is a great potential of using
YouTube data in a wide range of real-life applications. As a group of knowledge engineers, your
team is required to use knowledge creation and representation techniques to analysis available
YouTube data, for gaining an in-depth knowledge of user online activity. You will need to decide
one topic that is of your interest, and clearly state that in your report. The data structure from
YouTube is shown as follows:
Table. 1 Data structure for harvested YouTube content
| Columns/Attributes | Description | Columns/Attributes | Description |
| video_id | ID for a video | channel_title | Name of video channels |
| category_id | Type of the video | trending_date | Date of video trending |
| tags | Tags for the comments/videos |
views | How many views of the video |
| likes | The accumulated number of likes |
dislikes | The accumulated number of dislikes |
| comment_count | The accumulated number of comments until the publish_time |
description | Comments content |
Description of category_id:
| 1 – Film & Animation 2 – Autos & Vehicles 10 – Music 15 – Pets & Animals 17 – Sports 18 – Short Movies 19 – Travel & Events 20 – Gaming 21 – Videoblogging 22 – People & Blogs 23 – Comedy 24 – Entertainment 25 – News & Politics 26 – Howto & Style 27 – Education 28 – Science & Technology 29 – Nonprofits & Activism 30 – Movies 31 – Anime/Animation 32 – Action/Adventure 33 – Classics 34 – Comedy 35 – Documentary 36 – Drama 37 – Family 38 – Foreign 39 – Horror 40 – Sci-Fi/Fantasy 41 – Thriller 42 – Shorts 43 – Shows 44 – Trailers |
Your tasks:
1. Some related topics include, but not limited to:
the influence analysis from video channels (tips: identify popular video channels and explore
their influence in relation to type of video, likes/dislikes and received comments, etc., over
the time span)
sentiment analysis of comments (tips: find out the relationship between “likes” (“dislikes”)
and “description”)
NLG (nature language generator) (tips: find out the relationship between “tags” and
“description”)
categorising videos based on comments (tips: find out the relationship between
“category_id” and “description”)
prediction of video popularity (tips: find out the relationship between “views” and
“description, comment_count, category_id”, etc)
You need to choose a YouTube-related topic, and state it explicitly in your report.
2. Apart from the available datasets, it is expected that you collect other necessary information
and/or existing case studies from academic resources (such as journal papers and books) to
facilitate your research. This will be presented as the knowledge acquisition part in your project.
3. Various knowledge creation techniques can be employed including, but not limited to:
Classification (such as DT or ANN)
Clustering (such as SOM)
Association analysis (such as rule mining)
4. Finally, you need to write a report (maximum 2500 words) to elaborate on the following item:
Knowledge Acquisition or elicitation process
The techniques that you have employed for knowledge creation
o You need to justify the choice of techniques
o You need to provide at least 2 techniques to achieve full mark of knowledge
creation section
Results and Discussions
oThe information resource that you have gathered to assess the generated knowledge
oYou can compare and contrast each knowledge category that is generated in the previous
section with the existing documents or case studies from existing academic papers
oMinimum 2 pieces for each knowledge category are expected to achieve full mark
Explain and justify the possible inconsistencies in the gathered knowledge
Marking Criteria:
| Very good |
Good | Satisfacto ry |
Marginal | poor | ||
| Acquiring knowledge |
Through literature and the previous methods that have been applied |
6 | 5 | 4 | 3- 2.5 |
1.5 |
| Knowledge creation |
Justification of the methods chosen | 6 | 5 | 4 | 3- 2.5 |
1.5 |
| Software development –RapidMiner or other programming tools (marked online in lab) |
10 | 8 | 6 | 5-4 | 3 | |
| Presentation of the work in the report with explanation |
6 | 5 | 4 | 3- 2.5 |
1.5 | |
| Discussions and conclusion |
Compare and contrast each knowledge category that is generated in the previous section with the existing documents or case studies from existing academic papers |
8 | 6 | 5 | 4-3 | 2 |
| Report writing (presentation, quality of writing, writing style, spelling grammar and use of resources |
4 | 3 | 2.5 | 2- 1.75 |
1 |
[Button id=”1″]
[ad_2]
Source link
"96% of our customers have reported a 90% and above score. You might want to place an order with us."
