I am doing a Research work where the Market Capitalization needs to be Forecast for Two Quarters ahead.
We have news data for each quarter and using few labelled samples sentiment of each news is given a rating on 1-5 scale using KNIME Text Processing workflow.
This sounds like a case for some combination of the GroupBy and Math Formula nodes. Without a workflow or example of your data it’s hard to be more specific than that.
I will mail the data file and the problem I have somewhat explained in detail:
Let us take any Quarter of a particular year say Jan,Feb,March 2020.
Using machine learning let us suppose in the month of Jan 2020 there are THREE POSITIVE NEWS AND TWO NEGATIVE NEWS. In Feb 2020 ONE HIGHLY POSITIVE NEWS AND ONE HIGHLY NEGATIVE NEWS . In March 2020 THREE NEUTRAL and ONE NEGATIVE NEWS.
I would like a single sentiment to be computed for the entire THREE Months.
Simply I cannot add the sentiment directly: 3 POS + 2 NEG = 1 POS for Jan etc…
How to get the correct overall single sentiment score
Sounds like you need some sort of lookup table to assign scores to your different sentiments. Maybe highly positive = 2, neutral = 0, negative = -1 and so on.
If there is one record for each news article, you could assign scores with a Cell Replacer node, and then just sum up the scores over your period of interest with a GroupBy.
I am trying to compute overall sentiment in a Quarter. Instead of adding would like to do an RFV type of Analysis and assign weights to different buckets based on recency , Frequency and value will be sentiment score . How to do RFV type of Analysis in KNIME
You might take a look at this workflow by @tommy to see how he approached the problem. I’m not aware of much RFV analysis that’s been done in KNIME, so hopefully some other community members will chime in if they know of some.