Sena reads how consumers in each market feel about a category and its products, from what they state directly, and then sets that tone beside what they bought. Brand and analytics teams see direction and demand in a single read.
Sentiment reporting is widely adopted and lightly trusted. Teams read the number, discount it, and act on something else. Three specific weaknesses explain the discount.
A score built on public text measures the consumers who chose to write something. In most categories, that is a small and unrepresentative slice of the people buying.
A score says the mood softened. Whether anyone changed what they bought sits in a separate system, so the reading lands with its commercial consequence detached.
A positive, neutral, and negative split compresses every cause into one axis. The attribute driving the mood, which is the part a claim can answer, gets discarded in the scoring.
Sena is a decision AI with access to real-world data. It collects how consumers in each market feel about a category directly from those consumers, names the attribute behind the feeling, and reports it beside recorded purchases for the same window.
Sena asks the people buying the category, so the reading covers the quiet majority alongside the vocal minority.
Sena reports what consumers named as the cause, so the score arrives with something a claim can answer.
Sena reports a recorded purchase for the same window, so a shift in tone carries its commercial weight on arrival.
Sentiment analysis assigns direction to what people express about a product or category. The mechanics are settled. What separates a useful score from a decorative one is the source it reads and whether the purchase sits beside it.
Answer the attribute. The mood moved and buying followed it, held firmly enough to predict more switching. Name the cause and address it.
| Layer | What it produces | What it is worth |
|---|---|---|
| Polarity | Positive, neutral, negative | A direction, and little else |
| Attribute | The feature or claim driving the feeling | The part a message can answer |
| Intensity | How strongly the position is held | Whether it predicts a switch |
Name the products a buyer weighs together. A score computed across a boundary buyers ignore is a number with an empty referent.
Public posts and category buyers are different groups. Decide which one the score is meant to describe before reading it.
Capture the cause alongside the direction. A negative score with an unnamed cause leaves the team guessing.
Place the score beside recorded buying for the same market and window. A mood that moves demand and that leaves it flat look identical until then.
In marketing, the score answers whether a claim landed, whether a pack change was accepted, and whether a competitor's move shifted the category's feeling. Those are attribute questions, and a polarity number answers a different one.
Sena collects the stated cause with the direction and reports both per market.
The same phrase carries a second meaning in trading, where market sentiment measures investor mood and feeds price prediction. That work reads news flow, positioning, and volatility.
The two share a name and separate everywhere else. Everything here concerns consumers in a product category.
Real people share what they buy and prefer, under explicit consent. Sena captures it directly at the source, so every figure traces back to its source whenever a number comes into question.
Sena answers sentiment questions from consumer data collected under explicit consent. It names the attribute, the market, and the demand response and attaches the reasoning and the source records to the answer.
Sena runs the same reading in every market, so a mood shift in one country compares directly against another.
Sena reports the attribute consumers named, so a drop arrives with a response already implied.
Every sentiment figure traces to the consumers, the market, and the date behind it.
A sentiment figure is only as defensible as the people it came from. Sena produces every reading on this page from real-world data captured when the question needs it, from the consumer signal at the source through to the market, the date, and the source behind every figure.
Records what consumers bought while they felt what they said they felt. That activity is what a sentiment score gets checked against, drawn from the people making the category decision. Every score comes from a person who agreed to share what they think and buy. That consent covers the statement itself, so a score carries the words behind it.
Reads the products, packs, and claims present in real stores, so a shift in feeling reads against what changed on the shelf.
Brings the team's campaign, sales, and CRM data in through existing integrations, including Salesforce and HubSpot, so tone sits alongside the numbers reported each week.
Turns scattered sentiment exports, reviews, and spreadsheets into one connected database, so a current reading sits on the same trend line as every score already filed.
Four teams read the same score, and each one needs a different cut of it before they can act.
Owns perception and needs the attribute behind a shift before drafting a response.
Owns spend and needs to know whether a mood change touched demand.
Owns the claim and needs the feature consumers reacted to, named.
Owns the number in the review and needs it traceable to statements.
The same reading, checked against the decision each category actually makes.
Read the tone about the pack, price, and formulation alongside the recorded purchase, market by market.
Track how consumers feel about product terms across regions as they change.
Catch the feeling behind a plan change before it reaches the churn figure.
Find the feature driving the feeling, and check it against what buyers chose.
One mechanism, applied per market and per category. Each step is documented, which keeps the score defensible.
Real contributors state how they feel about the category and why, under explicit consent, in the markets under review.
Sena assigns direction, names the driving attribute, and records intensity per market.
Recorded purchase for the same window reports beside the score, so the tone arrives with demand.
Most sentiment setups score the people who posted. Sena asks the people who bought. The table below sets out where the two diverge.
| Capability area | Current approach | Sena |
|---|---|---|
| Who the score covers | Consumers who chose to write something publicly. | Consumers buying the category, including the quiet ones. |
| Depth of the score | Positive, neutral, and negative, with the cause compressed away. | Direction, the driving attribute, and intensity were reported together. |
| Coverage in quiet categories | Thin as the score depends on posting volume. | The collection reaches buyers directly in every market. |
| Demand alongside | Reported by a separate team on a separate cycle. | Recorded purchase for the same market and window, in one answer. |
| Market granularity | Regional aggregates that blend markets with opposite moods. | Each market scored separately, with its segments named. |
| Auditing a score | A model description and an aggregate figure. | Every point traces back to the statements, the market, and the date. |
Three situations where knowing the cause behind a mood changes the decision on the table. All three read from one consumer network.
Read the attribute consumers named, address it, and watch the score move back.
Compare tone before and after a claim went live in each market, with the attribute response named.
Track feelings about a reformulation or new pack from the week it hits shelves, and separate noise from demand effect.
Sentiment reports direction. The four decisions below run closest to it.
Every marketing decision Sena supports, in one place
Category attention, market by market
Share of voice counts how much of the conversation involves a product. Sentiment reads which way that conversation points.
Explore share of voice 02Awareness, consideration, and preference, per market
Sentiment moves first. Brand tracking reports whether the mood reached consideration and preference, market by market.
Explore brand tracking software 03What consumers did, and why
Sentiment names the feeling. Consumer analysis reports the purchase that followed it and the alternative that won.
Explore consumer behavior analysis 04The rival move behind a mood shift
Mood often shifts because somebody else moved. Competitor monitoring names the price, pack, or claim behind it.
Explore competitor monitoringThe walkthrough reports how consumers in the markets named feel about that category, names the attributes driving it, and opens the statements behind any score while the team watches.
Tell us the claim and the markets, and we will bring the reading.