Been testing a few sentiment analysis tools for our app reviews and support tickets.
Most give you basic positive/negative scores but I need something that actually tells me what to fix or improve.
What tools have you used that go beyond just scoring and give real actionable data?
MonkeyLearn worked well for one of my e-commerce apps. It breaks down sentiment by specific topics like checkout, shipping, UI. Shows you which parts of your product are actually frustrating users.
For support tickets, I used Lexalytics. It flags urgent issues and groups similar complaints. Saved me hours of reading through everything manually.
Both tools let you set up custom categories that match your business. Way better than generic positive/negative ratings.
This video covers keyword tracking methods that complement sentiment analysis pretty well:
The combination of sentiment analysis plus keyword tracking gives you the full picture of what users actually want fixed.
I prefer not to use complex sentiment tools. Instead, I gather reviews in a spreadsheet to find patterns myself.
It usually takes around 30 minutes to pinpoint the main issues that need addressing. You can prioritize based on how frequently users mention the same concerns.
Automated tools often overlook important context, so manual analysis can yield better insights for smaller datasets.
Skip the fancy sentiment tools. Set up keyword tracking instead. Pull all your reviews and tickets into one place, then track specific keywords like “crashes”, “slow”, “confusing”, “payment failed”. Count frequency over time. This shows you exactly what breaks most often and when it started happening. Way more actionable than sentiment scores. I use this method to prioritize bug fixes and feature updates. Takes 15 minutes to set up and gives you real direction on what to build next.
Just read reviews directly. Most tools overcomplicate simple feedback.
Some tools give insights but many just reiterate what users say. Look for ones emphasizing trends or specific recommendations.