SentimentInsights is a Ruby gem that helps you uncover meaningful insights from open-ended survey responses using Natural Language Processing (NLP). It supports multi-provider analysis via OpenAI, AWS Comprehend, or a local fallback engine.
Quickly classify and summarize user responses as positive, neutral, or negative — globally or by segment (e.g., age, region).
insight = SentimentInsights::Insights::Sentiment.new
result = insight.analyze(entries){:global_summary=>
{:total_count=>5,
:positive_count=>3,
:neutral_count=>0,
:negative_count=>2,
:positive_percentage=>60.0,
:neutral_percentage=>0.0,
:negative_percentage=>40.0,
:net_sentiment_score=>20.0},
:segment_summary=>
{:age=>
{"25-34"=>
{:total_count=>3,
:positive_count=>3,
:neutral_count=>0,
:negative_count=>0,
:positive_percentage=>100.0,
:neutral_percentage=>0.0,
:negative_percentage=>0.0,
:net_sentiment_score=>100.0}},
:top_positive_comments=>
[{:answer=>
"I absolutely loved the experience shopping with Everlane. The website is clean,\n" +
"product descriptions are spot-on, and my jeans arrived two days early with eco-friendly packaging.",
:score=>0.9}],
:top_negative_comments=>
[{:answer=>
"The checkout flow on your site was a nightmare. The promo code from your Instagram campaign didn’t work,\n" +
"and it kept redirecting me to the homepage. Shopify integration needs a serious fix.",
:score=>-0.7}],
:responses=>
[{:answer=>
"I absolutely loved the experience shopping with Everlane. The website is clean,\n" +
"product descriptions are spot-on, and my jeans arrived two days early with eco-friendly packaging.",
:segment=>{:age=>"25-34", :region=>"West"},
:sentiment_label=>:positive,
:sentiment_score=>0.9}]}}Extract frequently mentioned phrases and identify their associated sentiment and segment spread.
insight = SentimentInsights::Insights::KeyPhrases.new
result = insight.extract(entries, question: question){:phrases=>
[{:phrase=>"everlane",
:mentions=>["r_1"],
:summary=>
{:total_mentions=>1,
:sentiment_distribution=>{:positive=>1, :negative=>0, :neutral=>0},
:segment_distribution=>{:age=>{"25-34"=>1}, :region=>{"West"=>1}}}}],
:responses=>
[{:id=>"r_1",
:sentence=>
"I absolutely loved the experience shopping with Everlane. The website is clean,\n" +
"product descriptions are spot-on, and my jeans arrived two days early with eco-friendly packaging.",
:sentiment=>:positive,
:segment=>{:age=>"25-34", :region=>"West"}}]}Identify named entities like organizations, products, and people, and track them by sentiment and segment.
insight = SentimentInsights::Insights::Entities.new
result = insight.extract(entries, question: question){:entities=>
[{:entity=>"everlane",
:type=>"ORGANIZATION",
:mentions=>["r_1"],
:summary=>
{:total_mentions=>1,
:segment_distribution=>{:age=>{"25-34"=>1}, :region=>{"West"=>1}}}},
{:entity=>"jeans",
:type=>"PRODUCT",
:mentions=>["r_1"],
:summary=>
{:total_mentions=>1,
:segment_distribution=>{:age=>{"25-34"=>1}, :region=>{"West"=>1}}}},
{:entity=>"24 hours",
:type=>"TIME",
:mentions=>["r_4"],
:summary=>
{:total_mentions=>1,
:segment_distribution=>{:age=>{"45-54"=>1}, :region=>{"Midwest"=>1}}}}],
:responses=>
[{:id=>"r_1",
:sentence=>
"I absolutely loved the experience shopping with Everlane. The website is clean,\n" +
"product descriptions are spot-on, and my jeans arrived two days early with eco-friendly packaging.",
:segment=>{:age=>"25-34", :region=>"West"}},
{:id=>"r_4",
:sentence=>
"I reached out to your Zendesk support team about a missing package, and while they responded within 24 hours,\n" +
"the response was copy-paste and didn't address my issue directly.",
:segment=>{:age=>"45-54", :region=>"Midwest"}}]}Automatically group similar responses into topics and subthemes.
| Feature | OpenAI ✅ | AWS Comprehend ✅ | Sentimental (Local) |
|---|---|---|---|
| Sentiment Analysis | ✅ | ✅ | ✅ |
| Key Phrases | ✅ | ✅ | ❌ Not supported |
| Entities | ✅ | ✅ | ❌ Not supported |
| Topics | 🔜 Coming Soon | 🔜 Coming Soon | ❌ |
Legend: ✅ Supported | 🔜 Coming Soon | ❌ Not Available |
question = "What did you like or dislike about your recent shopping experience with us?"
entries = [
{
answer: "I absolutely loved the experience shopping with Everlane. The website is clean,\nproduct descriptions are spot-on, and my jeans arrived two days early with eco-friendly packaging.",
segment: { age: "25-34", region: "West" }
},
{
answer: "The checkout flow on your site was a nightmare. The promo code from your Instagram campaign didn’t work,\nand it kept redirecting me to the homepage. Shopify integration needs a serious fix.",
segment: { age: "35-44", region: "South" }
},
{
answer: "Apple Pay made the mobile checkout super fast. I placed an order while waiting for my coffee at Starbucks.\nGreat job optimizing the app UX—this is a game-changer.",
segment: { age: "25-34", region: "West" }
},
{
answer: "I reached out to your Zendesk support team about a missing package, and while they responded within 24 hours,\nthe response was copy-paste and didn't address my issue directly.",
segment: { age: "45-54", region: "Midwest" }
},
{
answer: "Shipping delays aside, I really liked the personalized note inside the box. Small gestures like that\nmake the Uniqlo brand stand out. Will definitely recommend to friends.",
segment: { age: "25-34", region: "West" }
}
]# Install the gem
$ gem install sentiment_insights
# Configure the provider
SentimentInsights.configure do |config|
config.provider = :openai # or :aws, :sentimental
end
# Run analysis
insight = SentimentInsights::Insights::Sentiment.new
result = insight.analyze(entries)
puts JSON.pretty_generate(result)OPENAI_API_KEY=your_openai_key_hereAWS_ACCESS_KEY_ID=your_aws_key
AWS_SECRET_ACCESS_KEY=your_aws_secret
AWS_REGION=us-east-1- Minimum Ruby version: 2.7
- Tested on: 2.7, 3.0, 3.1, 3.2
bundle exec rspec- Sentiment Analysis
- Key Phrase Extraction
- Entity Recognition
- Topic Modeling
- CSV/JSON Export Helpers
- Visual Dashboard Add-on
MIT License
Pull requests welcome! Please open an issue to discuss major changes first.
File an issue or reach out on GitHub