Difference between revisions of "APA Feature Engineering"
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We will be using subject line weightage as one of the components in determining how important and relevant a single email exchange is to the business. Our approach will be as follows: | We will be using subject line weightage as one of the components in determining how important and relevant a single email exchange is to the business. Our approach will be as follows: | ||
# First run an analysis on all the terms occurring in the entire dataset | # First run an analysis on all the terms occurring in the entire dataset | ||
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# Using the value of this tf-idf, we will assign each term a weightage based on how important it is in determining the importance to the business | # Using the value of this tf-idf, we will assign each term a weightage based on how important it is in determining the importance to the business | ||
# Each subject line of an email will then have an aggregated weightage of the terms appearing in itself. | # Each subject line of an email will then have an aggregated weightage of the terms appearing in itself. | ||
| − | < | + | <br> |
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| + | '''isCC? isForward? isReply?:'''<br> | ||
| + | * isCC?: Will indicate whether the email was CC-ed to this recipient | ||
| + | * isForward?: Will indicate whether the email was forwarded to the recipient | ||
| + | * isReply?: Will indicate whether the email was a reply | ||
Revision as of 16:33, 17 February 2017
Subject Line weightage: We will be using subject line weightage as one of the components in determining how important and relevant a single email exchange is to the business. Our approach will be as follows:
- First run an analysis on all the terms occurring in the entire dataset
- This analysis will filter out common words, prepositions and other unimportant words that could potentially skew the results.
- The analysis will return a listen of words along with the frequency of the term’s occurrence in the dataset.
- Based on the results obtained, we would like to calculate the tf-idf of each term.
- tf: how often does the term occur in the document
- idf: how often does the term occur in other documents
- tf-idf will allow us to find the most important terms in the set of documents
- Using the value of this tf-idf, we will assign each term a weightage based on how important it is in determining the importance to the business
- Each subject line of an email will then have an aggregated weightage of the terms appearing in itself.
isCC? isForward? isReply?:
- isCC?: Will indicate whether the email was CC-ed to this recipient
- isForward?: Will indicate whether the email was forwarded to the recipient
- isReply?: Will indicate whether the email was a reply
