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Daily tweets scored for sentiment and sent to Slack
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Overview
An n8n workflow that searches X every morning for posts with a hashtag, saves them in MongoDB and scores their sentiment with Google. The scores go into Postgres, and a positive post is sent to a Slack channel.
Daily tweets scored for sentiment and sent to Slack is an n8n workflow in the Marketing & SEO category, listed by Mindket. It is free. This is version 1, last updated on . Download it on this page or copy its JSON, then import it into an n8n canvas. Nothing runs until you activate it in n8n.
At a glance
- Runs in
- n8n Cloud: import the file into a new workflow there, with databases that n8n can reach from the internet
- Your own n8n server: the same file and steps, next to your own databases
- Another hashtag: change the search text in Twitter
- Another channel: change the channel name in Slack
Platform
How it works
Trigger
A daily schedule
It starts by itself:
- Every day at 6 am
- You can also run it by hand while you test
Actions
- X is searched for recent posts that use the hashtag #OnThisDay, ten at most
- Each post’s text is saved in a MongoDB collection
- Google Cloud Natural Language scores the sentiment of each post
- The text, the score and the magnitude are saved in a Postgres table
- A post with a score above zero is sent to a Slack channel, and the others stop there
Result
Rows in MongoDB and Postgres, and a Slack message for each positive post
Integrations
What you need before you start.
X
Post search
Searches recent posts that use the hashtag.
NeedsA developer app with credit, in an X OAuth2 credential
MongoDB
Database
Keeps the text of each post.
NeedsA MongoDB credential
Google Cloud Natural Language
Sentiment scoring
Scores how positive or negative each post is.
NeedsA Google Cloud project with the Natural Language API on, in a credential
PostgreSQL
Database
Keeps each post with its score and magnitude.
NeedsA Postgres credential and a table named tweets
Slack
Notification
Shows each positive post in a channel.
NeedsA Slack credential
Workflow check
Checked by Mindket on 13 Nov 2026
- Steps
- 9 steps, starting on a schedule
- AI
- No step calls an AI model
- Sign-ins
- Signs in to: X, Google Cloud Natural Language and Slack
- API keys
- No API key is left in the file
- Step types
- Mindket’s checker reads 7 of its 9 step types. It does not read: Mongo Db and Postgres
About
What it is
This n8n workflow checks X once a day for posts that use a hashtag. A schedule starts it at 6 am. It searches for #OnThisDay, takes up to ten posts and saves each post’s text in MongoDB. Google Cloud Natural Language scores the sentiment of each post. The text and the two scores go into Postgres. If the score is above zero, a Slack message shows the post. Nine nodes do the work.
Who it suits
Small teams who want a daily look at how people talk about a topic, plus a table of scores to study later. Skip it if you need every post on a topic. The search covers only the last seven days, in small batches.
| Industry | Who uses it | How they use it | Example |
|---|---|---|---|
| Marketing | Social media managers | Watch the mood around a hashtag each morning | A manager follows a campaign tag and sees the cheerful posts in Slack. |
| Media | Newsroom editors | Spot the tone of public posts on a topic | An editor tracks a history hashtag and keeps the scores in a database. |
| Research | Students | Collect a small sample of posts with sentiment scores | A student saves a week of posts and scores to chart the mood. |
| Brand | Community managers | Find posts worth thanking | A manager reads the positive posts in a Slack channel and replies to the best ones. |
You need some n8n skills and five accounts: X, MongoDB, Google Cloud, Postgres and Slack.
Where to use it
Any n8n that can reach X, Google Cloud, Slack and your two databases will run it.
- n8n Cloud: import the file into a new workflow there, with databases that n8n can reach from the internet
- Your own n8n server: the same file and steps, next to your own databases
- Another hashtag: change the search text in
Twitter - Another channel: change the channel name in
Slack
Setup
Download saves a zip with workflow.json and Mindket’s setup notes in your downloads folder. Five accounts are involved.
| Need | Detail |
|---|---|
| n8n | On n8n Cloud or on a server of your own |
| X | A developer app with credit for reading posts, saved in an X OAuth2 credential |
| MongoDB | A database, and a MongoDB credential for it |
| Google Cloud | A project with the Natural Language API turned on, and a Google Cloud Natural Language credential |
| Postgres | A database with a table named tweets that has the columns text, score and magnitude, and a Postgres credential |
| Slack | A Slack app that can post in the channel, and a Slack credential |
- In n8n, create a workflow, open its menu, choose Import from File and pick
workflow.json. - Add an X OAuth2 credential and select it in
Twitter. Change the search text if you do not want#OnThisDay. - Add a MongoDB credential and select it in
MongoDB. The collection is calledtweets. - Add a Google Cloud Natural Language credential and select it in
Google Cloud Natural Language. - Create the table
tweetsin Postgres, add a Postgres credential and select it inPostgres. - Add a Slack credential and select it in
Slack. Change the channel name if you do not usetweets. - Run the workflow once by hand, then activate it.
How to use it
Activate the workflow and it runs every morning at 6. Each run searches X, stores the posts, scores them and posts the positive ones in Slack. You can also run it by hand while you test.
| To change | Where |
|---|---|
| The search | searchText in Twitter |
| How many posts per run | limit in Twitter, which X accepts from 10 to 100 |
| The time of day | The hour in Schedule Trigger |
| What counts as positive | The comparison and value in IF |
| The Slack message | The text in Slack |
Example
A community manager sets the search text to the name of a product and activates the workflow.
Each morning the latest posts about the product land in MongoDB and Postgres with a score and a magnitude. Posts with a score above zero show up in the Slack channel with both numbers.
For newcomers and experts
If you are new. Sentiment analysis rates how positive or negative a text is. Google returns a score from -1 (negative) to 1 (positive). It also returns a magnitude that grows with the amount of emotion in the text. n8n draws a workflow as boxes joined by lines, and each box, a node, does one job.
For experts. The Twitter node is version 2 and calls X’s recent search endpoint. The Set node reads the post text from Twitter by node name, so a renamed node breaks it. The remaining nodes are version 1.
| Node | What it does | What to know |
|---|---|---|
Twitter | Searches X for recent posts with the hashtag | Recent search covers the last seven days and returns 10 to 100 posts per request |
MongoDB | Inserts the text of each post into the collection tweets | It saves only the text, with no author or date |
Google Cloud Natural Language | Scores the sentiment of each saved post | The score runs from -1 to 1 and the magnitude from 0 upward |
Set | Collects text, score and magnitude in one item | It reads the post text from Twitter by name |
Postgres | Inserts the row into the table tweets | The table must exist first |
IF | Checks whether the score is above 0 | The false route ends in an empty NoOp node |
Good to know
The search node and the trigger were replaced because the old ones no longer fit. Mindket did not run this workflow.
- Search node: the file used the version 1 Twitter node, which calls X’s older search address with an OAuth1 credential. X’s docs, read on 12 November 2026, map that address to recent search. n8n’s current node, version 2, calls it with an OAuth2 credential, and Mindket’s copy uses it
- Cost: X’s pricing page, read on 12 November 2026, lists post reads at 0.005 US dollars each on pay per usage, so ten posts a day cost about five cents. Recent search needs at least 10 results per request, so the limit went from 3 to 10
- Trigger: the file used the Cron node, which n8n hides in its node list. Mindket’s copy uses a Schedule Trigger at 6 am
- Repeats: recent search reaches back seven days, so the same post can come back on a later day and land in both databases again
- Older nodes: the Slack, Postgres, MongoDB and Google Cloud Natural Language nodes are still in n8n’s source as version 1, so Mindket kept them
- Your data: the post texts go to MongoDB, Google, Postgres and Slack. The file holds no credential
README and files
Daily tweets scored for sentiment and sent to Slack
An n8n workflow that searches X every morning for posts with a hashtag, saves them in MongoDB and scores their sentiment with Google. The scores go into Postgres, and a positive post is sent to a Slack channel.
What is inside
workflow.json: the workflow as n8n exports it, nine nodes. It holds no credential and no sticky notes.README.md: these notes.
What we changed from the original copy
- The search step was the version 1 Twitter node, which calls X’s older search address with an OAuth1 credential. X’s migration guide, read on 12 November 2026, maps that address to recent search. Mindket’s copy uses n8n’s version 2 node, which calls recent search with an OAuth2 credential.
- The search asked for 3 posts. X’s recent search takes at least 10 results per request, so it now asks for 10. At 0.005 US dollars a post read, that is about five cents a day.
- The Cron node, which n8n hides in its node list, became a Schedule Trigger that runs every day at 6 am.
- The
IFnode compared the score with an empty second value. The value is now an explicit 0. - Several nodes were linked to credentials in the author’s n8n. The links are gone, so select your own.
- Nothing else. The MongoDB, Natural Language, Set, Postgres and Slack steps are the file’s own.
Before you start
- An n8n instance, on n8n Cloud or your own server.
- An X developer app with credit for reading posts.
- A MongoDB database and a Postgres database with a table
tweetsthat has the columnstext,scoreandmagnitude. - A Google Cloud project with the Natural Language API turned on.
- A Slack app that can post in your channel.
Set it up
- In n8n, create a workflow, open its menu, choose Import from File and pick
workflow.json. - Add an X OAuth2 credential and select it in
Twitter. Change the search text if you do not want#OnThisDay. - Add a MongoDB credential and select it in
MongoDB. The collection is calledtweets. - Add a Google Cloud Natural Language credential and select it in
Google Cloud Natural Language. - Create the table
tweetsin Postgres, add a Postgres credential and select it inPostgres. - Add a Slack credential and select it in
Slack. Change the channel name if you do not usetweets. - Run the workflow once by hand, then activate it.
How a run goes
Schedule Triggerstarts the run every day at 6 am.Twittersearches X for recent posts with#OnThisDayand returns ten.MongoDBinserts the text of each post into the collectiontweets.Google Cloud Natural Languageanalyzes the sentiment of each text.Setkeeps the text, the score and the magnitude.Postgresinserts each row into the tabletweets.IFsends a post with a score above 0 toSlack, which posts it in the channeltweets. The others go to an emptyNoOpnode.
Change the result
- The search:
searchTextinTwitter. - How many posts per run:
limitinTwitter, which X accepts from 10 to 100. - The time of day: The hour in
Schedule Trigger. - What counts as positive: The comparison and value in
IF. - The Slack message: The text in
Slack.
Good to know
- Mindket did not run this workflow. X, MongoDB, Google, Postgres and Slack were not called. The node settings come from n8n’s source and the vendors’ documentation, read on 12 November 2026.
- Google’s documentation says the score runs from -1.0 (negative) to 1.0 (positive). The magnitude is not normalized, so a longer text can have a bigger one.
- X’s recent search reaches back seven days, and the same post can come back on a later day. The workflow does not check for repeats.
- The workflow does not create the Postgres table, so create it first.
- The MongoDB step stores only the post’s text, with no author, date or link.
- The post texts go to MongoDB, Google, Postgres and Slack.
- The file holds no credential and no key.
NOTICE.md 582 B README.md 4 KB workflow.json 5 KB
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