Sentiment Summary

Let take 1 record in the AHT LN file that had been scored.

CC_AHT LN_Q22022_6_14_2022

From the analysts

We have divided all words into three categories namely ‘Very Important’, ‘Important’ and ‘Less so important’ - please refer to attachment and the color coding. The category classification is to allow the student to ascribe a “weight” to each of the key words. For example, a key word that has been assign as ‘Very Important’ should have more weight/importance in the sentiment score than a key word that is ‘Less so important’.

 

Here’s an illustration of the weight for each of the category.

Very Important -> 2x

Important -> 1x

Less so Important -> 0.5x

Using these weights, the sentiment model score should be more “robust”.

What we have to figure out programmatically is how we want to handle the summary file. It is hard to specifically spec out without putting restrictions on the developers. Usually I give developers freedom to decide which is the best way to handle.

One method is:

If we have a scored paragraph

Key Words/Topics	Sector	Key Word Category
Inflation	All	Macro	Important
Interest rate	All	Macro	Important
Raw Materials inflation	Industrials	Macro	Less so important
Volume	All	Sector trend	Less so important
Revenue	All	Financial metric - All	Very Important
Earnings	All	Financial metric - All	Very Important
Earnings per Share / EPS	All	Financial metric - All	Very Important

So the paragraph

Key Word Category	Keyword	Paragraph	Sentiment Score
Financial metric - All	Revenue	So let's begin with highlights on Slide 3. Our business continues to perform very well, and experienced accelerated momentum throughout the year, demonstrating the strong levels of demand so clearly present. The strength delivered a record performance driven principally by a 23% increase in North America revenues which led to PBT of $1.8 billion at a 40% increase in earnings per share.	0.9398140907

Has a score of .93 because revenue is very important the weighted score for this would be 1.86 or 2x

When we do the summary and calculate all the scores we would use the ratio about to get weighted.

Yahoo Ticker	WM
document type	CC
period	Q123
Average score Macro	0.8
Number of times mentioned macro in document	10
Weighted average score of macro by importance		Calculated by the weigted value of the keywords found
Average score Sector trend	0.7
Number of times mentioned sector trend	12
Weighted average score sector trend by importance	.	Calculated by the weigted value of the keywords found
Average score Financial metric	0.5
Number of times mentioned Financial metric	11
Weighted average financial metric by importance		Calculated by the weigted value of the keywords found
Average score Regulation	0.9
Number of times mentioned regulation	8
Weighted average regulation score		Calculated by the weigted value of the keywords found
Total score average	Average unweighted scores
Total score weighted	Average weigted scores
Read thru all detail records for indivdual CC and put counters in place for category of field and calculate average score for category
Check the keyword for how it should be weighted so we can cal
increment counters as you scan thru all the records in the one call
when all detail records are counted write summary record
example below is one detail record
Key Word Category	Keyword	Paragraph	Sentiment Score
Financial metric - All	Revenue	So let's begin with highlights on Slide 3. Our business continues to perform very well, and experienced accelerated momentum throughout the year, demonstrating the strong levels of demand so clearly present. The strength delivered a record performance driven principally by a 23% increase in North America revenues which led to PBT of $1.8 billion at a 40% increase in earnings per share.	0.9398140907
Key Words/Topics	Sector	Key Word Category
Revenue	All	Financial metric - All	Very Important
Revenue is very important so we weight it 2x the value
Very Important -> 2x