User Sentiment Trend Analysis
This dataset provides granular sentiment analysis of user-generated social media posts and interactions, including engagement metrics, language, and location data. It enables platforms and analysts to track sentiment trends over time, correlate sentiment with user engagement, and visualize sentiment patterns across multiple social networks. Ideal for brand monitoring, market research, and user experience optimization.
Sample rows
preview · 8 of 200 rows · all 16 columns| sentiment_idstring | sentiment_labelstring | sentiment_scorefloat | topicstring | user_idstring | post_idstring | platformstring | post_datetimedatetime | languagestring | engagement_likesinteger | engagement_commentsinteger | engagement_sharesinteger | location_citystring | location_statestring | location_countrystring | post_textstring |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| twtr-8kzQmN4v | positive | 0.73 | #NextLevel | user_001 | post_twtr_101 | 2024-06-04T15:39:22Z | en | 289 | 19 | 32 | San Francisco | CA | US | Excited to announce our new partnership! #NextLevel | |
| fbk-Yz49W3Rd | negative | -0.79 | product_review | user_002 | post_fbk_201 | 2024-06-02T10:12:44Z | en | 6 | 2 | 1 | London | blank | GB | Product review: disappointed with the recent update. | |
| ig-2wFh9J7L | positive | 0.26 | #teamwork | user_003 | post_ig_301 | 2024-06-07T20:47:31Z | en | 120 | 15 | 12 | Sydney | NSW | AU | Teamwork makes the dream work. Proud of my crew! #teamwork | |
| lnkd-uT4X5g7f | positive | 0.99 | #career | user_004 | post_lnkd_401 | 2024-06-03T09:22:05Z | en | 832 | 32 | 44 | Toronto | ON | CA | Just got promoted! Grateful for the opportunity. #career | |
| rddt-4t9kLc3v | neutral | 0.05 | update | user_005 | post_rddt_501 | 2024-06-10T01:18:59Z | en | 4 | 1 | 0 | Berlin | blank | DE | Any thoughts on the recent update? Looking for feedback. | |
| twtr-1VbX7p2z | negative | -1 | outage | user_006 | post_twtr_102 | 2024-06-08T07:43:11Z | en | 13 | 4 | 2 | New York | NY | US | Outage again. Service keeps dropping. Really frustrating! | |
| fbk-Zk2x5B8M | neutral | -0.2 | conference | user_007 | post_fbk_202 | 2024-06-12T11:56:01Z | en | 7 | 1 | 0 | Paris | blank | FR | Conference coming up next week. Who's attending? | |
| ig-9hT2Vw7Q | positive | 1 | #summer | user_008 | post_ig_302 | 2024-06-01T14:31:16Z | en | 250 | 18 | 20 | Miami | FL | US | Amazing day at the beach! #summer | |
| lnkd-1kQ8r7Lp | positive | 0.48 | #NextLevel | user_009 | post_lnkd_402 | 2024-06-11T16:44:33Z | en | 321 | 23 | 17 | Chicago | IL | US | Excited to join the new project team! #NextLevel | |
| rddt-xL7B2v9K | negative | -0.21 | layoffs | user_010 | post_rddt_502 | 2024-06-15T19:53:22Z | en | 2 | 1 | 0 | Seattle | WA | US | Layoffs at the company. Morale is low right now. | |
| twtr-2Rk6mJ4b | neutral | 0.2 | review | user_011 | post_twtr_103 | 2024-06-09T18:11:25Z | en | 8 | 2 | 0 | Austin | TX | US | Just finished reading the new report. Mixed feelings. | |
| fbk-5Lq2yV8z | positive | 0.22 | support | user_012 | post_fbk_203 | 2024-06-05T09:28:10Z | en | 19 | 3 | 2 | Vancouver | BC | CA | Great experience with the support team. Thanks! | |
| ig-8nB7cV2M | negative | -0.55 | #disappointed | user_013 | post_ig_303 | 2024-06-13T08:05:29Z | en | 5 | 1 | 1 | Rome | blank | IT | Very disappointed with the latest app update. | |
| lnkd-3pN6wF9x | positive | 0.48 | conference | user_014 | post_lnkd_403 | 2024-06-14T12:17:36Z | en | 112 | 14 | 11 | Boston | MA | US | Honored to speak at the annual conference. #career | |
| rddt-7rB3cJ8v | neutral | 0 | career | user_015 | post_rddt_503 | 2024-06-02T21:06:49Z | en | 1 | 0 | 0 | Madrid | blank | ES | Any advice for remote work productivity? | |
| twtr-6Nq3wT5k | negative | -0.99 | outage | user_016 | post_twtr_104 | 2024-06-06T12:05:44Z | de | 2 | 1 | 0 | Berlin | blank | DE | Service outage in Berlin. Not happy about this. | |
| fbk-9Tq2cY5z | neutral | 0.19 | update | user_017 | post_fbk_204 | 2024-06-16T13:43:22Z | en | 3 | 0 | 0 | Dublin | blank | IE | Hoping for improvements in next update. | |
| ig-4pJ8hK9n | positive | 0.99 | #success | user_018 | post_ig_304 | 2024-06-17T17:35:10Z | en | 9999 | 999 | 1000 | Los Angeles | CA | US | Our team is unstoppable! #teamwork #success | |
| lnkd-2qK7wH3c | positive | 0.73 | #hiring | user_019 | post_lnkd_404 | 2024-06-18T08:22:40Z | en | 500 | 35 | 60 | Manchester | blank | GB | We're hiring new talent! Join us. #hiring | |
| rddt-8kB9cV2m | neutral | -0.18 | review | user_020 | post_rddt_504 | 2024-06-19T23:16:07Z | en | 0 | 0 | 0 | blank | blank | blank | Review: Decent performance but could be better. |
What the 200 rows show
from the 200-row sampleNegative (sentiment label) stands out: mean sentiment_
- 0.19median sentiment_
score - 3languages
- 6platforms
- 11location countries
- 12location states
- 25location cities
Median 0.19, from -1.0 to 1.0.
- string 11
- integer 3
- float 1
- datetime 1
Columns
16 columns in three groups| column | type | description | example |
|---|---|---|---|
| Text 11 columns | |||
sentiment_id | string | Unique identifier for each sentiment analysis recordunique | twtr-8kzQmN4v |
user_id | string | Unique identifier for the user who posted or interacted | user_001 |
post_id | string | Unique identifier for the social post or interaction | post_twtr_101 |
platform | string | Name of the social media platform (e.g., Twitter, Facebook)6 values | |
sentiment_label | string | Categorical sentiment label derived from the scorepositive · neutral · negative | positive |
post_text | string | Full text content of the social post or interaction | Excited to announce our n… |
language | string | Language code of the post (e.g., en, es, fr)3 languages | en |
topic | string | Primary topic or hashtag associated with the postoptional | #NextLevel |
location_city | string | City associated with the user's location, if availableoptional | San Francisco |
location_state | string | State or region associated with the user's location, if available12 states · optional | CA |
location_country | string | Country code associated with the user's location, if available11 countries · optional | US |
| Numbers 4 columns | |||
sentiment_score | float | Numerical sentiment score for the post, typically between -1 (negative) and 1 (positive)-1 to 1 | 0.73 |
engagement_likes | integer | Number of likes or upvotes received by the post0 or more · optional | 289 |
engagement_comments | integer | Number of comments or replies to the post0 or more · optional | 19 |
engagement_shares | integer | Number of shares or retweets of the post0 or more · optional | 32 |
| Dates and times 1 column | |||
post_datetime | datetime | Timestamp when the post or interaction was created | 2024-06-04T15:39:22Z |
Use it for
A social media dashboard
Sentiment_
score by sentiment_ label and a breakdown of topic. Excel, Power BI or Tableau. Why do the 53 negative rows have a mean sentiment_
score of -0.74? A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
- Sentiments200twtr-8kzQmN4v0.73positivefbk-Yz49W3Rd-0.79negativeig-2wFh9J7L0.26positive
A software demo
Believable sentiments with user_
id, post_ id and platform to fill a screen in front of a buyer.
This dataset200 rows16 columns
Yours1,000 rows16 columnslocation_city: UK only
blueprint · user-sentiment-trend-analysis
Behind this dataset
Same schema. As many rows as you need.
These 200 rows came out of a blueprint — 16 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.
Rules it was built with
- Include sentiment label and post ID
- Record date and platform
- Flag significant trend changes
- Exclude bots and spam
Open the blueprint in Data Factory
1 credit per row. Preview 20 rows free. Buy rows for the full run.
- Exports
- CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
- Licence
- yours to use, including commercially
- API slug
- user-sentiment-trend-analysis