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| 11 | + <title>Youtube API Data Trend</title> |
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| 18 | + <a id="forkme_banner" href="https://github.com/pengyangwu/CS1951a">View on GitHub</a> |
| 19 | + <h1 id="project_title">Blog Post #1</h1> |
| 20 | + <h2 id="project_tagline">CS1951a : Data Science Project</h2> |
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| 22 | + <section id="downloads"> |
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| 32 | + <h2> |
| 33 | +<a id="command-line-youtube-data" class="anchor" href="#" aria-hidden="true"><span class="octicon octicon-link"></span></a>Youtube API Data Trend</h2> |
| 34 | + |
| 35 | +<h4> |
| 36 | +<a id="author-Aaron-Abhishek-Natalie-Preston-Wennie" class="anchor" href="#" aria-hidden="true"><span class="octicon octicon-link"></span></a>Author: Aaron Wu (pwu8), Abhishek Dutta (adutta2), Natalie Roe (nroe), Preston Law (plaw), Wennie Zhang (yzhang46)</h4> |
| 37 | + |
| 38 | + |
| 39 | +<h3> |
| 40 | +<a id="content"><span class="octicon octicon-link"></span></a>This Week's Work</h3> |
| 41 | +<p> Up until out first TA check-in, we've collected the data that we will be using from YouTube and have stored it in a database. |
| 42 | + Below are a few images of the data that we've collected: </p> |
| 43 | + <h4><a id="content"><span class="octicon octicon-link"></span></a>Comment Feed Data</h4> |
| 44 | + <img src="images/youtube_video_data.png" align="middle"> |
| 45 | + <p> In terms of the video itself, we were able to obtain quite a lot of information about it including |
| 46 | + the channel it came from, the title, time it was published at and the thumbnail.</p> |
| 47 | + |
| 48 | +<p> As you can see, there's quite a lot of data for us to work with, so we're hoping to be able to draw some |
| 49 | + interesting conclusions from our analysis of the data. A few questions we are considering answering in our |
| 50 | + project are: </p> |
| 51 | + <ul> |
| 52 | + <li> What makes a video go viral, and how can we predict that this will happen? </li> |
| 53 | + <li> What video length appeals most to users? Is there a point at which viewers become disinterested |
| 54 | + in the video due to its length? </li> |
| 55 | + <li> How is a user's profile related to the videos that they view? </li> |
| 56 | + <li> How does a video's thumbnail affect the number of views the video receives? </li> |
| 57 | + <li> Do certain topics coincide with major historical events? </li> |
| 58 | + <li> Are there consistent themes in high traffic areas? </li> |
| 59 | + </ul> |
| 60 | +<p> The overarching theme of the analysis that |
| 61 | + we will be doing for this project is predicting future trends on YouTube based on historical data, and the questions listed |
| 62 | + above are just a few of the ways we may go about performing out analysis. Clearly, some questions may have a more |
| 63 | + obvious answer than others (analyzing the thumbnail would be really difficult) they are all interesting questions to consider |
| 64 | + and we would be able to create some nice visualizations from them too. |
| 65 | + |
| 66 | + |
| 67 | + |
| 68 | +<h3> |
| 69 | +<a id="content"><span class="octicon octicon-link"></span></a>Analysis Topic</h3> |
| 70 | +<p> For our next blog post, we hope to have our web application that allows users to search for YouTube trends pertaining to a particular |
| 71 | + topic to be mostly fleshed out, including well-tested topic visualization and user interaction with past trends within our |
| 72 | + chosen category. We should also have our theme forecaster and analysis ready for integration with the main web app.</p> |
| 73 | + |
| 74 | + |
| 75 | +<p>Home Page # is: <a href="https://pengyangwu.github.io/CS1951a/"> here </a>.</p> |
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