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* Update generate_terminal_markdown.py * updates * update workflows * Automated Change * tests * tests * Automated Change * Update test_generate_docs.py * Automated Change * updates * Automated Change * Update gen-docs.yml * Update test.yml * Automated Change * Update test.yml * Automated Change * Update generate_sdk_markdown.py * Automated Change * test * Automated Change * testtt23 * Automated Change * Update generate_sdk_markdown.py * Automated Change * Automated Change * added empting reference folders before autogen docs * updates * Automated Change * update tests gitactions * Automated Change * up * Automated Change * Automated Change * update pytest * Automated Change * tests * Automated Change * examples update * Automated Change * Update generate_sdk_markdown.py * Automated Change * Update test.yml * Update generate_sdk_markdown.py * strip * Automated Change * Workflow : check test update * Automated Change * Automated Change * Automated Change * Update test.yml * Automated Change * Update test.yml * Automated Change * Automated Change * up * Automated Change * Update generate_sdk_markdown.py * Automated Change * update workflows * fix guides * testing * Update gh-pages.yml * Update gh-pages.yml Co-authored-by: tehcoderer <tehcoderer@users.noreply.github.com> Co-authored-by: Jeroen Bouma <jer.bouma@gmail.com> Co-authored-by: Chavithra PARANA <chavithra@gmail.com> Co-authored-by: Chavithra <Chavithra@users.noreply.github.com> Co-authored-by: jose-donato <43375532+jose-donato@users.noreply.github.com> Co-authored-by: jose-donato <jose-donato@users.noreply.github.com>
924 B
Vendored
924 B
Vendored
title, description
| title | description |
|---|---|
| infer | OpenBB Terminal Function |
infer
Print quick sentiment inference from last tweets that contain the ticker. This model splits the text into character-level tokens and uses vader sentiment analysis. [Source: Twitter]
Usage
infer [-l LIMIT]
Parameters
| Name | Description | Default | Optional | Choices |
|---|---|---|---|---|
| limit | limit of latest tweets to infer from. | 100 | True | None |
Examples
2022 Feb 19, 13:05 (🦋) /stocks/ba/ $ infer
From: 2022-02-19 17:08:20
To: 2022-02-19 18:04:18
100 tweets were analyzed.
Frequency of approx 1 tweet every 34 seconds.
The summed compound sentiment of AAPL is: 13.2
The average compound sentiment of AAPL is: 0.13
Of the last 100 tweets, 45.00 % had a higher positive sentiment
Of the last 100 tweets, 18.00 % had a higher negative sentiment