Board path
Listening > Sentiment > Overview
Why use this board
Use this board to track sentiment trends for your listening queries; see how positive, negative, and neutral mention mix shifts over time.
Board controls
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Select data source: Select Listening Queries or Listening Query Sets. In order to use another data source type, please delete the current one and add a different data source type.
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Adaptive filters: Open Filter in the toolbar to narrow posts by Platform, Content Label, Sentiment, Language, Country, Interest, Media Type, Content Type, Full-text, or Advanced filter. Active filters appear as chips below the toolbar; remove a chip to clear that filter.
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Date range: Defaults to the last 30 days. The board subtitle shows the active range when available.
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Aggregated by: Day, Week, Month, Quarter, Year.
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Export: Export the table to XLSX, including the selected date range when available.
Metrics on this board
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Frequency — Top positive themes
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Number of mentions — Sentiment of mentions trend
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Frequency — Top positive keywords
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Frequency — Top negative keywords
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Number of mentions — Sentiment of mentions trend
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Frequency — Top negative themes
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Mentions — Sentiment of mentions summary
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Authors — Sentiment of mentions summary
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Interactions — Sentiment of mentions summary
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Potential Impressions — Sentiment of mentions summary
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Number of mentions — Sentiment of mentions by platform
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Mentions — Topics summary
Widgets
Top positive themes
What it measures: An overview of most frequently detected themes with a Postivie sentiment among mentions matching the analyzed queries. A single mention can have multiple themes. You can think of themes as detailed points that can be grouped under interests. Our machine learning system can detect up to 10,000 different themes in mentions containing text in English, Spanish, Portuguese, and Czech. Shown as a word cloud — larger labels mean higher frequency; use it to spot the most common themes, keywords, or hashtags in your data.
Metrics: Frequency
Visualization: Word cloud
Sentiment of mentions trend
What it measures: An overview of the mentions matching the analyzed queries, segregated by sentiment (Positive, Negative, Neutral) and content publication date. Mentions discovered by multiple analyzed queries are counted only once. Only mentions with a detected sentiment are included in the chart. Shown as a trend over time — use it to spot spikes, drops, and seasonality across the selected period.
Metrics: Number of mentions
Visualization: Line chart
Top positive keywords
What it measures: An overview of the most frequently featured keywords with a Positive sentiment among mentions matching the analyzed queries. Shown as a word cloud — larger labels mean higher frequency; use it to spot the most common themes, keywords, or hashtags in your data.
Metrics: Frequency
Visualization: Word cloud
Top negative keywords
What it measures: An overview of the most frequently featured keywords with a Negative sentiment among mentions matching the analyzed queries. Shown as a word cloud — larger labels mean higher frequency; use it to spot the most common themes, keywords, or hashtags in your data.
Metrics: Frequency
Visualization: Word cloud
Sentiment of mentions trend
What it measures: An overview of the mentions matching the analyzed queries, segregated by sentiment (Positive, Negative, Neutral) and content publication date. Mentions discovered by multiple analyzed queries are counted only once. Only mentions with a detected sentiment are included in the chart. Shown as stacked bars — each segment shows a category's share of the total; use it to see composition, not just the headline number.
Metrics: Number of mentions
Visualization: Stacked bar chart
Top negative themes
What it measures: An overview of most frequently detected themes with a Negative sentiment among mentions matching the analyzed queries. A single mention can have multiple themes. You can think of themes as detailed points that can be grouped under interests. Our machine learning system can detect up to 10,000 different themes in mentions containing text in English, Spanish, Portuguese, and Czech. Shown as a word cloud — larger labels mean higher frequency; use it to spot the most common themes, keywords, or hashtags in your data.
Metrics: Frequency
Visualization: Word cloud
Sentiment of mentions summary
What it measures: An overview of the distribution of sentiment (e.g. Positive, Negative, Neutral) among mentions matching the analyzed queries. This table also shows the number of mentions, authors, interactions, and potential impressions per sentiment. Only mentions with a detected sentiment are included in the chart.
Metrics: Mentions, Authors, Interactions, Potential Impressions
Visualization: Table
Sentiment of mentions by platform
What it measures: The distribution of sentiment (e.g. Positive, Negative, Neutral) for mentions matching the analyzed queries, segregated by platform (e.g. social media network, blog, forum, news site, etc.). Mentions discovered by multiple analyzed queries are counted only once. Only mentions with a detected sentiment are included in the chart. Shown as stacked bars — each segment shows a category's share of the total; use it to see composition, not just the headline number.
Metrics: Number of mentions
Visualization: Stacked bar chart
Topics summary
What it measures: This table summarizes all detected topics among mentions matching the analyzed queries, as well as the sentiment and contextual words most frequently associated with them
Metrics: Mentions
Visualization: Table