UX: Make sentiment trends more readable (#1018)
Instead of a stacked chart showing a separate series for positive and negative, this PR introduces a simplification to the overall sentiment dashboard. It comprises the sentiment into a single series of the difference between `positive - negative` instead. This should allow for the data to be more easy to scan and look for trends
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@ -400,9 +400,7 @@ en:
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sentiment:
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sentiment:
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reports:
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reports:
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overall_sentiment:
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overall_sentiment: "Overall sentiment (Positive - Negative)"
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positive: "Positive"
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negative: "Negative"
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post_emotion:
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post_emotion:
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sadness: "Sadness 😢"
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sadness: "Sadness 😢"
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surprise: "Surprise 😱"
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surprise: "Surprise 😱"
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@ -11,7 +11,7 @@ module DiscourseAi
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sentiment_count_sql = Proc.new { |sentiment| <<~SQL }
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sentiment_count_sql = Proc.new { |sentiment| <<~SQL }
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COUNT(
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COUNT(
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CASE WHEN (cr.classification::jsonb->'#{sentiment}')::float > :threshold THEN 1 ELSE NULL END
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CASE WHEN (cr.classification::jsonb->'#{sentiment}')::float > :threshold THEN 1 ELSE NULL END
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) AS #{sentiment}_count
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)
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SQL
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SQL
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grouped_sentiments =
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grouped_sentiments =
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@ -19,8 +19,7 @@ module DiscourseAi
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<<~SQL,
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<<~SQL,
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SELECT
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SELECT
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DATE_TRUNC('day', p.created_at)::DATE AS posted_at,
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DATE_TRUNC('day', p.created_at)::DATE AS posted_at,
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#{sentiment_count_sql.call("positive")},
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#{sentiment_count_sql.call("positive")} - #{sentiment_count_sql.call("negative")} AS sentiment_count
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-#{sentiment_count_sql.call("negative")}
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FROM
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FROM
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classification_results AS cr
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classification_results AS cr
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INNER JOIN posts p ON p.id = cr.target_id AND cr.target_type = 'Post'
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INNER JOIN posts p ON p.id = cr.target_id AND cr.target_type = 'Post'
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@ -32,30 +31,26 @@ module DiscourseAi
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cr.model_used = 'cardiffnlp/twitter-roberta-base-sentiment-latest' AND
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cr.model_used = 'cardiffnlp/twitter-roberta-base-sentiment-latest' AND
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(p.created_at > :report_start AND p.created_at < :report_end)
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(p.created_at > :report_start AND p.created_at < :report_end)
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GROUP BY DATE_TRUNC('day', p.created_at)
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GROUP BY DATE_TRUNC('day', p.created_at)
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ORDER BY 1 ASC
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SQL
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SQL
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report_start: report.start_date,
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report_start: report.start_date,
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report_end: report.end_date,
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report_end: report.end_date,
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threshold: threshold,
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threshold: threshold,
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)
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)
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data_points = %w[positive negative]
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return report if grouped_sentiments.empty?
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return report if grouped_sentiments.empty?
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report.data =
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report.data = {
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data_points.map do |point|
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req: "overall_sentiment",
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{
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color: report.colors[:lime],
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req: "sentiment_#{point}",
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label: I18n.t("discourse_ai.sentiment.reports.overall_sentiment"),
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color: point == "positive" ? report.colors[:lime] : report.colors[:purple],
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label: I18n.t("discourse_ai.sentiment.reports.overall_sentiment.#{point}"),
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data:
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data:
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grouped_sentiments.map do |gs|
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grouped_sentiments.map do |gs|
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{ x: gs.posted_at, y: gs.public_send("#{point}_count") }
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{ x: gs.posted_at, y: gs.public_send("sentiment_count") }
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end,
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end,
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}
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}
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end
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end
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end
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end
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end
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end
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end
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end
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end
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end
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end
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@ -77,11 +77,8 @@ RSpec.describe DiscourseAi::Sentiment::EntryPoint do
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sentiment_classification(pm, positive_classification)
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sentiment_classification(pm, positive_classification)
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report = Report.find("overall_sentiment")
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report = Report.find("overall_sentiment")
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positive_data_point = report.data[0][:data].first[:y].to_i
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overall_sentiment = report.data[:data][0][:y].to_i
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negative_data_point = report.data[1][:data].first[:y].to_i
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expect(overall_sentiment).to eq(0)
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expect(positive_data_point).to eq(1)
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expect(negative_data_point).to eq(-1)
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end
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end
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end
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end
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