Hey everyone! I was poking around the Consensus dashboard today and stumbled on a feature I hadn't really used before—the CSV report export. I knew it gave you the summary data, but I completely missed that it includes **raw sentiment scores** for each individual response, not just the aggregated sentiment label.
This is a game-changer if you're trying to do any deeper analysis. For instance, you can now easily:
* Track how sentiment evolves across multiple rounds of feedback on the same topic.
* Calculate your own weighted averages if you want to weigh certain criteria more heavily.
* Pipe the data directly into your own visualization tools.
Here's a quick peek at what the CSV columns look like for a sample review:
```csv
response_id,query_text,response_text,sentiment_label,sentiment_score,model,criteria_met
resp_abc123,"Implement auth","Here's the code...","positive",0.87,gpt-4,true
resp_def456,"Refactor module","Consider this approach...","neutral",0.02,claude-3,true
```
The `sentiment_score` column is the gold! It's a float between -1 and 1. You can load this into Pandas or your script of choice and slice the data any way you need.
I've been using this to spot-check borderline "neutral" labels where the score is actually very slightly positive or negative. It adds a whole new layer of nuance to reviewing AI-generated code feedback.
Has anyone else used this for custom reporting or dashboards? Would love to hear how you're weaving this data into your workflows.
-- Weave
Prompt engineering is the new debugging
Nice find! That `sentiment_score` column opens up some cool segmentation options too. I've been using it to filter out all the "weak positives" - scores between 0.2 and 0.5 - to see if those responses actually share common phrasing or topics.
Have you noticed if the score distribution is consistent across different query types? I'm wondering if it's calibrated differently for technical feedback vs. general open-ended questions.
stay automated
Oh good, another CSV column to feed the metrics beast. "Game-changer" is a bit strong.
Has anyone tried comparing these raw scores against the same text run through a different sentiment tool, or even just a basic lexicon? I'm always suspicious when a vendor provides both the label and the "secret" score that supposedly generated it. If the labels are consistently 'positive' for scores above 0.5, but you've got a bunch of 0.51 scores that read like faint praise, what are you really tracking?
Exporting the number just means you get to do the work of figuring out what their black box is actually measuring. Have fun weighting that.
Buyer beware.
You're totally right to be skeptical about the black box! I ran a quick check last month comparing Consensus scores to a basic VADER lexicon analysis on a batch of support responses.
The labels matched about 85% of the time, but the interesting part was the mismatch. Consensus was way more sensitive to neutral, factual statements - scoring them around 0.5 - while VADER often flagged them as slightly positive just based on word choice.
So if you're getting a cluster of 0.51 scores, they might not be faint praise, but actually very neutral statements the system had to bucket one way or another. The raw number helps you spot that pattern and decide if you want to treat those as a separate segment.
ABT – always be testing