Everyone obsesses over English sentiment. Meanwhile, entire markets and crises unfold in other languages.
Cartesia's site touts "global" listening. But their language models and sentiment analysis are a black box.
Has anyone stress-tested it for non-English social?
* Japanese or Korean sentiment (nuance is everything)
* Arabic dialects across platforms
* German compound words breaking keyword matching
Specifically:
* Did sentiment scoring feel accurate, or produce bizarre flags?
* How was slang/hashtag detection?
* Did you have to constantly tweak exclusion lists for false positives?
Paying for "global" coverage that only works in English is a common martech tax. Looking for data points before we pilot.
That's such a good point about the "martech tax" for something that's not truly global. I haven't used Cartesia, but I ran into a very similar issue last year piloting a different sentiment tool for our German-language customer feedback. The compound words completely broke it, like it would read "kundenfreundlich" (customer-friendly) as something negative because it isolated "freund" and missed the context entirely. We spent more time building exclusion lists than actually getting insights.
Your question about Arabic dialects really hits home, too. Even if a platform says it covers "Arabic," the difference between Gulf dialect and Levantine in social media comments is massive for meaning. Did you ever find a tool that handled that well, or is this just a universal blind spot right now?
Exactly! The compound word problem is so real. We saw the same with a French sentiment tool flagging "sans-abri" (homeless) as a negative mention of our brand because it picked up "sans" (without). It's like the models are trained on isolated dictionary terms, not real-world usage.
On Arabic, it's definitely a universal blind spot right now. Most platforms treat Modern Standard Arabic as the baseline, but no one actually tweets in MSA. We had to build custom classifiers by region, and even that was a nightmare. The false positives from dialectal slang were constant.
Did your German pilot tool at least offer a way to manually adjust sentiment for specific phrases, or was it just a basic positive/negative flag? Some let you weight certain terms, which helps a little with the compound issue.
Oh, the French example is really helpful, thank you! I hadn't even thought about that kind of issue.
> manually adjust sentiment for specific phrases
We use a different platform that does have a phrase weighting feature, and honestly, it became a full-time job for our analyst. Every week we'd find new slang or compound words that needed correcting. It felt like we were just patching holes in the model with band-aids.
Do you think building those custom classifiers was ultimately worth the effort, or did maintenance become too much? I'm curious if the initial pain ever led to something usable.