# 8 Sentiment Analysis Tools and the Data Each One Reads (https://www.socialcrawl.dev/blog/sentiment-analysis-tools)
> Google's top AI answer still names MonkeyLearn, folded into Medallia back in 2022. Here are 8 sentiment analysis tools that exist, and what each can read.
Ask Google for a sentiment analysis tool and the first thing on the page, above every organic result, is an AI-written summary recommending [MonkeyLearn](https://monkeylearn.com/sentiment-analysis/). Medallia acquired MonkeyLearn in May 2022 and folded it into its own experience suite. The standalone no-code product that summary describes is not something you can go and sign up for.
That is a fair picture of this category. The advice is stale, and the tools that are current split into two camps that never quite meet. On one side, social listening suites that cost four figures a month and give you sentiment as one tile on a dashboard. On the other, raw NLP engines that cost about a dollar per million characters and give you a score with no interface at all.
Below are eight that are real, current, and priced in public. The column that matters most is the one every other roundup leaves out: what each tool can actually read. Four of the eight cannot fetch a single social post on their own.
The eight tools below split cleanly in half. Four are listening suites
(Brandwatch, Sprout Social, Awario, Hootsuite's free analyzer) that collect
posts for you and score them, priced from free to four figures a month. Four
are text-scoring engines (AWS Comprehend, Google Cloud Natural Language, Azure
AI Language, VADER) that are far cheaper per unit but will not fetch anything:
you supply the text. All three major clouds land near $1 per million
characters, but because Azure bills per record rather than per character,
scoring a million tweets runs about $200 on AWS versus about $1,000 on Azure.
## What to look for in a sentiment analysis tool
Four questions separate these tools far more than any feature list will:
- **Does it get the text, or do you?** This is the real dividing line. A listening suite runs its own collection. An NLP API is a scoring function waiting for input you have to supply.
- **How is a billing unit defined?** "One dollar per thousand units" means nothing until you know whether a unit is 100 characters, 1,000 characters, or one record of any length. For short social text that definition swings the bill by 5x.
- **How does it score negative sentiment specifically?** Most published accuracy figures are dominated by the positive class. Negative recall is where these models fall apart, and negative is usually what you actually care about.
- **Was it built for social text?** Emoji, slang, ALL CAPS, and trailing punctuation carry real signal in a tweet and none in a support ticket. Tools trained on reviews and surveys tend to flatten all of it.
## The 8 sentiment analysis tools at a glance
| # | Tool | Collects posts itself? | Best for | Price (Aug 2026) |
|---|------|------------------------|----------|------------------|
| 1 | Brandwatch | Yes | Enterprise brand tracking | From ~$800/mo, mostly custom |
| 2 | Sprout Social | Yes | Teams already managing social | From $249/mo per user |
| 3 | Awario | Yes | Small teams on a budget | From $29/mo billed annually |
| 4 | Hootsuite brand sentiment analyzer | Yes | A free one-off gut check | Free |
| 5 | AWS Comprehend | No | Cheapest at social-post scale | $0.0001 per 100-char unit |
| 6 | Google Cloud Natural Language | No | Entity-level sentiment | $1 per 1,000 units, 5k free/mo |
| 7 | Azure AI Language | No | Aspect-based opinion mining | $1 per 1,000 records, 5k free/mo |
| 8 | VADER and friends | No | Social text, zero budget | Free, open source |
### 1. Brandwatch
The enterprise default, and priced like it.
**What it does:** Collects mentions across social, news, forums and reviews, then scores sentiment and slices it by topic, demographic and time.
**Standout feature:** Historical archives. If you need to answer "how did sentiment move during the 2023 recall", Brandwatch has the back-catalogue that cheaper tools do not.
**Limitations:** Pricing is quote-driven and reported to start around $800 per month, climbing to six figures annually for global brands. Sentiment is one component of a large suite you may not otherwise need.
**Best for:** Brand and comms teams with budget who need defensible, board-ready reporting.
### 2. Sprout Social
Sentiment bolted onto a publishing and engagement suite.
**What it does:** Social management first, with sentiment analysis available through its listening module.
**Standout feature:** If your team already schedules and replies in Sprout, sentiment arrives in the same place as the rest of the workflow rather than in a separate tab.
**Limitations:** The pricing structure catches people out. Plans start at $249 per user per month, and social listening is a separate add-on commonly quoted between $2,000 and $8,000 per year on top. Sentiment is not part of the base subscription.
**Best for:** Teams that already run their social operation on Sprout.
### 3. Awario
The mid-market option that does not require a sales call.
**What it does:** Monitors mentions across social and web, breaks them down by sentiment, and reports reach.
**Standout feature:** Transparent, self-serve pricing in a category that hides it. Plans start at $29 per month billed annually, with higher tiers around $149 and $399 for more topics and mention volume.
**Limitations:** Mention volume caps bite quickly on a broad keyword. Sentiment is polarity only, so you get positive, negative and neutral without emotion or aspect breakdowns.
**Best for:** Startups and small teams that want brand sentiment tracking without an enterprise contract.
### 4. Hootsuite's free brand sentiment analyzer
The fastest way to get a number with no account.
**What it does:** [Hootsuite's free tool](https://www.hootsuite.com/social-media-tools/sentiment-analysis-tool) takes a brand or topic and returns a sentiment read on recent public conversation.
**Standout feature:** No signup, no card, no integration. It answers "is this on fire right now" in under a minute.
**Limitations:** It is a lead magnet, so it is deliberately shallow. No history, no export, no API, no control over the sample it draws from. Treat the output as directional, not as a measurement.
**Best for:** A one-off check, or convincing a stakeholder that a deeper look is worth funding.
### 5. AWS Comprehend
The cheapest credible option once you are working at social-post scale.
**What it does:** A managed NLP API returning sentiment (positive, negative, neutral, mixed) with confidence scores, plus entities, key phrases and language detection.
**Standout feature:** The billing unit. [Comprehend](https://aws.amazon.com/comprehend/pricing/) charges $0.0001 per 100-character unit with a 100-character minimum. Short texts stay cheap because you are billed close to what you actually send.
**Limitations:** It will not fetch anything. It also returns document-level sentiment by default, so a post praising the camera and slating the battery comes back as "mixed" with no indication of which feature drove which half.
**Best for:** High-volume pipelines over short text, where per-character billing compounds in your favour.
### 6. Google Cloud Natural Language
Sentiment attached to entities rather than whole documents.
**What it does:** Returns document sentiment plus a score and magnitude, and separately scores sentiment for each entity it detects in the text.
**Standout feature:** Entity-level sentiment. In one call you can learn that a post is positive about the price and negative about the delivery, which document-level scoring collapses into a single misleading number.
**Limitations:** [Pricing](https://cloud.google.com/natural-language/pricing) is $1 per 1,000 units after the first 5,000 units per month, and a unit is up to 1,000 characters. Anything shorter than 1,000 characters still consumes a full unit, so tweets and comments are billed as though they were essays.
**Best for:** Longer-form text such as reviews and survey responses, where you want to know what specifically people liked.
### 7. Azure AI Language
Aspect-based opinion mining, with a billing model that punishes short text.
**What it does:** Sentiment plus opinion mining, which links each sentiment to the aspect it refers to.
**Standout feature:** Opinion mining is the most explicit aspect-level output of the three clouds. It returns the target ("battery"), the assessment ("terrible"), and the polarity, as separate fields.
**Limitations:** Billing is $1 per 1,000 text records with 5,000 free per month, and a record is up to 1,000 characters regardless of how short it is. A 150-character tweet costs exactly what a 1,000-character review costs.
**Best for:** Product teams mining longer reviews where aspect-level detail justifies the rate.
### 8. VADER and the open-source stack
Free, self-hosted, and purpose-built for exactly the text the paid tools handle worst.
**What it does:** [VADER](https://github.com/cjhutto/vaderSentiment) is a rule-based scorer tuned for social media. It reads emoji, slang, capitalisation and punctuation emphasis as signal rather than noise. TextBlob is the simpler general-purpose alternative; Hugging Face transformer models are the accurate, heavier one.
**Standout feature:** Cost and transparency. It runs locally at zero marginal cost, and because it is rule-based you can inspect exactly why a post scored the way it did.
**Limitations:** Accuracy is lopsided in a way the headline figures hide. In [one published comparison](https://www.researchgate.net/publication/383841055_Sentiment_polarity_analysis_of_love_letters_Evaluation_of_TextBlob_Vader_Flair_and_Hugging_Face_transformer) on emotionally complex text, TextBlob scored 97.1% on positive passages but only 22.9% on negative ones, while VADER managed 84.3% and 43.5%. That is a single domain rather than a universal benchmark, but the shape of the result is consistent with what practitioners report: negative recall is the weak point, and sarcasm defeats all of them, transformers included.
**Best for:** Developers with volume, a tolerance for tuning, and short social text.
## The part none of these eight solve
Look back at the table. Four of these tools bundle collection and scoring, and charge accordingly. The other four are pure scoring functions, cost a fraction as much, and are frequently more accurate on the text you care about.
The catch is the one thing nobody's roundup mentions: the moment you pick a cheap, accurate scorer, you own the hard half of the problem. Getting a clean, deduplicated, current stream of posts out of TikTok, Instagram, Reddit, YouTube and X is not a scripting afternoon. Every platform has a different auth model, a different pagination scheme, a different field name for the same number, and a different opinion about how much of it you may have. That work is why the listening suites can charge $800 a month for a dashboard tile.
This is the gap SocialCrawl fills. It is not a sentiment tool and it does not pretend to be one: it is the data layer that feeds whichever scorer you picked above. One schema, every platform, so a post is a post whether it came from Reddit or TikTok, and the text field is in the same place either way. You pair that with Comprehend or VADER and you have the listening-suite pipeline at NLP-API prices.
Worth being clear about the trade-off. Assembling collection plus scoring yourself is genuinely more work than buying Brandwatch, and if you need historical archives going back years, buy the suite. The build-it path wins on cost and control at volume, not on time-to-first-chart.
## Which one should you actually pick?
- **You need a number this afternoon, for free:** Hootsuite's analyzer. Do not build anything.
- **You are a small team tracking your own brand:** Awario. It is the only self-serve tier in the listening group that does not require a sales conversation.
- **You are an enterprise with compliance and history requirements:** Brandwatch, or Sprout if your team already lives there.
- **You have volume and short social text:** AWS Comprehend on per-character billing, or VADER if the budget is zero and you can absorb the accuracy hit on negatives.
- **You need to know which feature people hated:** Azure AI Language for opinion mining, or Google Cloud Natural Language for entity-level scores.
## Frequently asked questions
### What is the best free sentiment analysis tool?
For a one-off check with no setup, Hootsuite's free brand sentiment analyzer returns a read on recent public conversation without an account. For anything repeatable, VADER is the better free option: it is open source, runs locally at zero marginal cost, and is specifically tuned for social media text including emoji, slang and capitalisation. The trade-off is that VADER requires you to supply the text yourself and is noticeably weaker at classifying negative sentiment than positive.
### How much does a sentiment analysis API cost?
The three major cloud APIs all land near $1 per million characters at list price as of August 2026, but the billing units differ in ways that matter. AWS Comprehend charges $0.0001 per 100-character unit, Google Cloud Natural Language charges $1 per 1,000 units where a unit is up to 1,000 characters, and Azure AI Language charges $1 per 1,000 records where a record is up to 1,000 characters. Because Google and Azure bill a full unit regardless of length, scoring a million 150-character tweets costs roughly $200 on AWS and roughly $1,000 on Azure or Google.
### Can sentiment analysis detect sarcasm?
Not reliably. Sarcasm remains an unsolved problem across rule-based scorers and transformer models alike, because the literal words carry the opposite polarity to the intended meaning. Rule-based tools such as VADER interpret sarcastic text literally by design. Transformer models trained on large corpora do better on some phrasings but still miss the majority of sarcastic social posts. If sarcasm is common in your data, treat aggregate trends as meaningful and individual post scores as unreliable.
### What is the difference between sentiment analysis software and a social listening tool?
A social listening tool collects mentions across social platforms and the web, then scores them. Sentiment analysis software, in the narrow sense, only scores text you provide. Brandwatch, Sprout Social, Awario and Hootsuite handle both collection and scoring, which is what their pricing reflects. AWS Comprehend, Google Cloud Natural Language, Azure AI Language and VADER are scoring engines only: they are dramatically cheaper per unit but will not fetch a single post on their own.
### How accurate is sentiment analysis on social media posts?
Accuracy is highly asymmetric between classes, which headline figures obscure. In one published comparison on emotionally complex text, TextBlob classified 97.1% of positive passages correctly but only 22.9% of negative ones, while VADER managed 84.3% and 43.5% respectively. That study covers a single domain rather than serving as a universal benchmark, but the pattern it shows is widely reported: models are far better at recognising positive sentiment than negative. Since negative sentiment is usually the reason teams run the analysis, test any tool on your own labelled sample before trusting it.
## Wrapping up
The honest summary of this category in 2026: sentiment scoring has been commoditised to roughly a dollar per million characters, while collecting the text has not been commoditised at all. That is why listening suites still command four figures a month for a capability three cloud providers will sell you for pennies.
If you are buying, pick from the top four and stop thinking about it. If you are building, the scorer is the easy decision, and it is worth spending your attention on the pipeline that feeds it. [SocialCrawl's API](/docs) covers that side, and the [visual explorer](/explorer) will show you the response shape before you write anything. If you want the wider landscape first, the [social media scraping API comparison](/blog/best-social-media-scraping-apis-2026) covers the collection layer in more depth.