SentimentInsights

gem 'sentiment_insights'

require 'sentiment_insights'

# For OpenAI
SentimentInsights.configure do |config|
  config.provider = :openai
  config.openai_api_key = ENV["OPENAI_API_KEY"]
end

# For AWS
SentimentInsights.configure do |config|
  config.provider = :aws
  config.aws_region = 'us-east-1'
end

# For sentimental
SentimentInsights.configure do |config|
  config.provider = :sentimental
end

entries = [
  { answer: "Amazon Checkout was smooth!", segment: { age_group: "18-25", gender: "Female" } },
  { answer: "Walmart Shipping was delayed.", segment: { age_group: "18-25", gender: "Female" } },
  { answer: "Target Support was decent.", segment: { age_group: "26-35", gender: "Male" } },
  { answer: "Loved the product!", segment: { age_group: "18-25", gender: "Male" } }
]

insight = SentimentInsights::Insights::Sentiment.new
result = insight.analyze(entries)

custom_prompt = <<~PROMPT
  For each of the following customer responses, classify the sentiment as Positive, Neutral, or Negative, and assign a score between -1.0 (very negative) and 1.0 (very positive).

            Reply with a numbered list like:
            1. Positive (0.9)
            2. Negative (-0.8)
            3. Neutral (0.0)
PROMPT

insight = SentimentInsights::Insights::Sentiment.new
result = insight.analyze(
  entries,
  question: "How was your experience today?",
  prompt: custom_prompt,
  batch_size: 10
)

{: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}]}}

insight = SentimentInsights::Insights::KeyPhrases.new
result = insight.extract(entries)

key_phrase_prompt = <<~PROMPT.strip
  Extract the most important key phrases that represent the main ideas or feedback in the sentence below.
  Ignore stop words and return each key phrase in its natural form, comma-separated.

  Question: %{question}

  Text: %{text}
PROMPT

sentiment_prompt = <<~PROMPT
  For each of the following customer responses, classify the sentiment as Positive, Neutral, or Negative, and assign a score between -1.0 (very negative) and 1.0 (very positive).

            Reply with a numbered list like:
            1. Positive (0.9)
            2. Negative (-0.8)
            3. Neutral (0.0)
PROMPT

insight = SentimentInsights::Insights::KeyPhrases.new
result = insight.extract(
  entries,
  question: "What are the recurring themes?",
  key_phrase_prompt: key_phrase_prompt,
  sentiment_prompt: sentiment_prompt
)

{: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"}}]}

insight = SentimentInsights::Insights::Entities.new
result = insight.extract(entries)

entity_prompt = <<~PROMPT.strip
  Identify brand names, competitors, and product references in the sentence below.
  Return each as a JSON object with "text" and "type" (e.g., BRAND, PRODUCT, COMPANY).

  Question: %{question}

  Sentence: "%{text}"
PROMPT

insight = SentimentInsights::Insights::Entities.new
result = insight.extract(
  entries,
  question: "Which products or brands are mentioned?",
  prompt: entity_prompt
)

{: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"}}]}