Overview

The Overview tab presents the headline reading on every analytical dimension of the conversation your query matched: how much of it there is, how it is received, which emotions and subjects run through it, when it happened, and which entities circulate inside it. Each element condenses a dimension that has a dedicated tab of its own, so this page is where an analysis usually starts, and the dedicated tabs are where it continues when one dimension warrants a closer look. The feed beside the results shows the posts behind the numbers, labeled on this tab with their sentiment.

The page adapts to the number of queries in the query bar. With a single query it shows the layout described in the next section. When two or more queries are active, a toggle appears above the results with three views: Compare sets the queries against each other, Aggregate combines them into one conversation, and Select focuses on one of them at a time. For the query bar itself, including syntax, time ranges, and display names, see Getting Started.

Single query (Select)

This is the layout you see with one query in the query bar. It is also what the Select view renders when several queries are active: choosing a query from the Select dropdown shows this same page for that query alone, which is useful for inspecting one subject in full without removing the others.

Stats bar

Four cells summarize the conversation before the charts break it down.

Total Volume is the number of posts matching the query in the selected time range. It is the most direct measure of how much attention the subject commands, and it calibrates every other reading: percentages and scores computed over a handful of posts deserve less weight than the same figures over tens of thousands. Against a reference point it also works as a trend instrument, since the same query run over different ranges, or checked again next week, shows whether the subject is gaining or losing attention.

Sentiment is the overall sentiment level, such as Mostly Positive or Very Negative, with the numeric score beneath it. It is the one-glance verdict on how the subject is being received; the two sentiment charts below show what the verdict is made of.

Top Emotion is the most frequently detected emotion, with its share of all emotions detected. The share tells you whether that emotion defines the conversation or merely edges out the rest: a 40% share marks a conversation with one dominant feeling, while a 15% share marks an even spread where the leader barely leads. The Emotions volume chart below shows the full field.

Top Topic is the most common subject category, with its share of all topics detected. It names the arena the conversation is happening in, and it doubles as a check on the query itself: a brand query expected to surface Business discussion but led by Culture or Daily Life may be matching a different audience, or a different meaning of the term, than intended. When the arena is not where the subject should live, the fix is usually a sharper query, such as an exclusion or an exact phrase, rather than a different conclusion.

Sentiment score

The gauge places the conversation's sentiment score on a scale from -100 to +100, with its level label beneath. The score is the balance of opinion among posts that take a side: the number of positive posts minus the number of negative ones, divided by their combined total. At +100 every opinionated post is positive, at -100 every one is negative, and near 0 the two sides are evenly matched. Neutral posts stay out of this calculation, so the score reads the direction of opinion without being diluted by indifferent posts; how much of the conversation is neutral is what the distribution chart beside the gauge shows.

Scores map to five levels used across the dashboard: Very Positive above +50, Mostly Positive from +25 to +50, Neutral from -25 to +25, Mostly Negative from -50 to -25, and Very Negative below -50.

A single reading answers how the subject is being received right now. The score becomes more informative with a reference point: against another time range it shows whether reception is improving or deteriorating, and against another query it shows how the subject fares relative to a competitor or benchmark. For how individual posts are classified as positive, negative, or neutral, see the sentiment analysis guide.

Sentiment distribution

The pie chart divides matching posts into positive, neutral, and negative; pointing at a segment shows its count and share. Unlike the score, the distribution includes neutral posts, which in many social media conversations form the largest block.

Read it to judge how much weight the score can carry. A strong score over a thin opinionated slice, visible as a large neutral segment, reflects few voices and can swing quickly; the same score over a broadly opinionated conversation reflects settled sentiment. The distribution also disambiguates a near-zero score, which can mean indifference (a dominant neutral segment) or a polarized standoff (large positive and negative segments balancing out). When the standoff pattern appears, the conversation is contested rather than calm, and the feed shows what each side is arguing.

The gauge shows which way opinion leans; the pie chart shows how much opinion there is.

Emotions volume

One bar per emotion category (Love, Joy, Anticipation, Anger, Disgust, Sadness, Fear, and Neutral for posts with no detectable emotion) shows how many posts express each, and pointing at a bar shows the exact count. The muted track behind the bars represents all matching posts, so each bar reads as that emotion's reach within the conversation. A post can express several emotions at once, so the bars measure how widespread each emotion is rather than splitting posts into exclusive groups, and their counts can add up to more than the total.

Emotions describe the character of opinion where sentiment describes its direction, and the difference tends to matter most when sentiment is negative. Anger points to criticism aimed at a decision or actor, Fear to worry about consequences, Sadness to disappointment, and each may warrant a different response even at the same score. The emotion classification guide explains what each category captures, and the Emotions tab adds how this composition shifts over time.

Topics volume

The same layout for subject matter: one bar per topic category (News, Business, Technology, Sports, Culture, Lifestyle, Daily Life, and Misc) against the total. As with emotions, a post can fall under several topics.

The topic mix frames every other measurement, since a negative score carried by News coverage and one carried by Daily Life chatter describe different situations with different audiences. Movements in sentiment or volume often trace back to one topic slice growing or shrinking, and the Topics tab shows that composition over time.

Volume over time

One bar per interval shows when the conversation happened. The toggle switches between three readings of the same series:

• Volume is the raw post count per interval. Check it whenever another number looks unusual: a single high-volume day can dominate every average on the page, and knowing whether sentiment formed gradually or arrived with one spike changes how the score should be read.
• Rate of Change is the difference between each interval and the one before, increases in green and decreases in red. It shows momentum directly: a sustained run of green marks a conversation still building even before its raw bars look tall, while alternating colors suggest ordinary day-to-day variation rather than a developing story.
• Cumulative is the running total from the start of the range. A straight diagonal means a steady pace, a steepening curve means acceleration, and a flattening curve means the burst has run its course. This is the clearest view for deciding whether a spike is over or still unfolding.

The Volume tab adds peak, average, and trend statistics along with a day-of-week activity breakdown.

Word cloud

The cloud shows the entities appearing most often in matching posts, sized by frequency, with a toggle for four entity types: Hashtags, Cashtags (ticker symbols such as $AAPL), Mentions (accounts referenced in posts), and Links (the sites posts link out to).

Where the charts describe the conversation in aggregate, the cloud names what circulates inside it: the campaigns and communities behind Hashtags, the stocks and tokens behind Cashtags, the people and organizations being addressed in Mentions, and the sources passed around as Links. Scanning it often explains what is driving the numbers above, and it is the natural source of the next, narrower query.

Clicking an entry appends it to the query in the query bar. The analysis does not rerun on its own, so adjust the query if needed and run it to narrow the results to posts that also contain that entry. Repeating this moves from a broad subject to one specific thread inside it. The Explore tabs expand each entity type with a treemap view, a full ranked table, and query actions for each entry.

Aggregate

Aggregate combines all active queries into one result set (a post matching several queries is counted once) and renders the same stats bar and charts described above over the combined conversation.

Use it when the queries are facets of one subject rather than rivals: alternate spellings, a brand plus its product names, or several phrasings of the same issue. Compare answers how the terms differ; Aggregate answers what the conversation looks like as a whole.

Compare

Compare sets the active queries side by side in every element of the page. Each query keeps the color and display name it has in the query bar across all stats and charts, and all queries are measured over the same time range with the same method, so differences between them reflect the conversations rather than artifacts of timing.

Stats bar

The four cells turn into cross-query verdicts, each naming a winning query in its color.

Highest Volume names the query with the most matching posts, with the count. This is the share-of-attention verdict: which subject the public talks about most. It also calibrates the rest of the comparison, since shares and scores from low-volume queries rest on fewer posts and deserve more caution.

Most Positive names the query with the highest sentiment score, with the score. It answers the comparison's most common question, which subject is received best, in one line. Because the score does not depend on volume, the ranking is fair between conversations of very different sizes; whether a lead is meaningful is something to confirm in the Sentiment chart below, where the Distribution view shows how much opinion stands behind each score.

Top Emotion names the strongest emotional concentration anywhere in the comparison: the emotion with the largest share within any single query, with that share and the query it belongs to. A standout concentration, such as one subject running 40% Anger while nothing else comes close, is usually the first thing in the comparison worth investigating.

Top Topic does the same for subject categories, flagging the strongest topical concentration and which query it belongs to.

Volume

One bar per query shows total matching posts on a shared scale, making the attention gap visible at a glance. The gap itself is a finding: a challenger generating half an incumbent's conversation tells a different story than one generating two percent of it. Keep these totals in mind when reading the composition charts below, which normalize each query to its own total and therefore hide the difference in scale shown here.

Sentiment

One bar per query, with a toggle between two views. Score plots each query's sentiment score on the -100 to +100 scale around a zero line; bars on opposite sides of the zero line mark genuinely opposed receptions rather than different degrees of the same one. Distribution stacks each query's bar to 100% with its shares of positive, neutral, and negative posts, showing how opinionated each conversation is. Reading the two together applies the same score-plus-distribution judgment as the single-query layout: a score lead built on a small opinionated minority is visible only in the second view.

Emotions

One bar per query, stacked to 100% and segmented by emotion category. Normalizing the bars makes emotional composition comparable even when volumes differ widely. Look for the segment that differs most between bars: two subjects with similar sentiment scores can run on entirely different feelings, such as one fueled by Anticipation and the other by Anger, and that difference often says more about the two conversations than the scores do. To inspect the posts behind a standout segment, switch to that query in the Select view or open the Emotions tab.

Topics

The same normalized layout segmented by topic category. It shows whether the compared subjects occupy the same arena at all: two competitors may both lean negative while one is debated as business news and the other as a cultural argument, which changes what their scores mean and who is doing the talking. When the topic mixes differ sharply, the sentiment and emotion comparisons are comparing different kinds of conversation, and their gaps should be read with that in mind. Reading a few posts per query in the feed clarifies what is being compared.

Volume over time

The time series stacks the queries within each interval, with a toggle between three readings:

• Volume stacks raw counts, showing the combined conversation and each query's contribution to it. When the combined total spikes, the segment colors show which query drove the surge.
• Distribution normalizes each interval to 100%, showing each query's share of the conversation regardless of how the total moves. This is the view for shifts in relative attention: one subject's share steadily expanding at another's expense stays visible even while the overall total swings.
• Cumulative stacks each query's running total, showing which subject accumulates conversation faster across the whole range and whether the gap between them is widening or closing, independent of any single day's noise.

Word cloud

The cloud combines the most frequent entries from every query, each colored by the query it belongs to, with the same four entity-type toggles. Color makes shared and distinctive vocabulary visible: entries in a single color name what that conversation circulates on its own, and a term that appears for more than one query marks common ground between them. Clicking an entry appends it to all queries at once, which keeps the comparison aligned on the refined scope; run the updated queries to apply it.