Feature: new meeting subject line mining functions

[martinctc]

Summary

This branch introduces several new features for text mining subject lines.

Changes

The changes made in this PR are:

1. Added subject_scan(). (Feature request: top subject words by hour #172)
2. Added subject_classify() (Feature request: meeting_classify() by subject line #93)
3. Added the ability to specify ngram tokenization settings in tm_clean().
4. Refurbished meeting_tm_report() in terms of look and feel, as well as using the new generate_report2() underlying implementation. Note that the current merge does not contain the proposed changes as listed below.

Next steps: proposed changes

1. In meeting_tm_report(), have word clouds coloured by HR attribute, or time of day, or day of week
2. Include word clustering - either by supervised subject classification or unsupervised text clustering - in meting_tm_report()
3. Include the ability to weight keywords in subject_scan()
4. Have meeting_tm_report() classify pages by 'Total', 'ROB', 'Admin', 'Other', etc., and replicate tabs of text visualization for each page

Proposed Flow for meeting_tm_report()

• Step 1: Start with all words
• Step 2: Remove stop words - > Total Words
• Step 3: Classify Total words into mutually exclusive categories:
  • Ways of Working: Weekly, Monthly, Annual, Review, Catch-up, Management, Stand-up, 1:1, etc.
  • Admin: Benefits, invoice, Travel, Flight
  • Training and Coaching: Learning, Coursera
    Step 4: Visualize in the report with a page for total words and a page per subset

Example

The following visual is produced with this code:

mt_data %>% subject_scan(hrvar = "Organizer_Organization")

Checks

• All R CMD checks pass
• roxygen2::roxygenise() has been run prior to merging to ensure that .Rd and NAMESPACE files are up to date.
• NEWS.md has been updated.

Notes

This fixes #93, #172.