Difference between revisions of "Group04 Final"
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==<div style="height: 15px; background: #f2f2f2; padding: 10px; font-weight: bold; line-height: 15px; text-indent: 5px; margin-bottom:; border-left: #d9d9d9 solid 5px; font-size: 15px; font-family: Arial;"><span style="color: #5F93D0;">Overview</span></div>== | ==<div style="height: 15px; background: #f2f2f2; padding: 10px; font-weight: bold; line-height: 15px; text-indent: 5px; margin-bottom:; border-left: #d9d9d9 solid 5px; font-size: 15px; font-family: Arial;"><span style="color: #5F93D0;">Overview</span></div>== | ||
| − | + | In this section, we will be using nonparametric statistical tests and text analysis to understand factors that affect the performance of content. Having a clear understanding on the factors of content performance will enable the company to determine its future strategy to continuously strive for better performance. | |
| + | |||
| + | We will explore posting times and content as factors of performance and seek an appropriate methodology to analyze their effects on content performance. To capture a wide range of audiences, the company is currently active on Facebook and YouTube. We will thus be looking at data scraped from Facebook and YouTube. | ||
| + | |||
| + | For the Facebook Post dataset, the performance of posts will be compared across posting time to determine if specific posting times will affect performance, while text analysis will be performed on consumers’ comments from the Facebook Comment dataset and YouTube dataset to identify if different surfaced topics will result in differing sentiments. After our literature review, we have chosen Topic Modeling and Sentiment Analysis as the preferred methodologies for text analysis. Also, the Median test will be used to compare performance across different posting times. | ||
===Facebook Posts=== | ===Facebook Posts=== | ||
Revision as of 13:55, 8 April 2018
| GROUP4 |
 HOMEPAGE
|
 PROJECT OVERVIEW
|
 PROJECT FINDINGS
|
 PROJECT MANAGEMENT
|
 DOCUMENTATION
|
 ANALY482 MAIN
|
| PROPOSAL | INTERIM | FINAL |
|---|
Contents
Overview
In this section, we will be using nonparametric statistical tests and text analysis to understand factors that affect the performance of content. Having a clear understanding on the factors of content performance will enable the company to determine its future strategy to continuously strive for better performance.
We will explore posting times and content as factors of performance and seek an appropriate methodology to analyze their effects on content performance. To capture a wide range of audiences, the company is currently active on Facebook and YouTube. We will thus be looking at data scraped from Facebook and YouTube.
For the Facebook Post dataset, the performance of posts will be compared across posting time to determine if specific posting times will affect performance, while text analysis will be performed on consumers’ comments from the Facebook Comment dataset and YouTube dataset to identify if different surfaced topics will result in differing sentiments. After our literature review, we have chosen Topic Modeling and Sentiment Analysis as the preferred methodologies for text analysis. Also, the Median test will be used to compare performance across different posting times.
Facebook Posts
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Facebook Comments
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Youtube
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Methods and Analysis
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Facebook Posts
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Methodology
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Results
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Business Insights
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Facebook Comments
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Methodology
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Results
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Business Insights
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YouTube
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Methodology
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Results
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Business Insights
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