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OpenBB/website/content/common/behavioural_analysis/infer/_index.md
Jeroen Bouma 3c9c2b9223 Complete check on Hugo Documentation (#1358) 
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* This is not a function on CA

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* This command does not exist on CA

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* Type in dcf help text

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* Fix missing index

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* doesn't exists

The command 'disp' doesn't exist on the /stocks/options/ menu.

2022 Feb 16, 08:49 (✨) /stocks/options/ $ disp
usage: options {calc,yf,tr,info,pcr,load,exp,vol,voi,oi,hist,chains,grhist,unu,plot,parity,binom,vsurf,greeks,payoff,pricing,screen,cls,home,h,?,help,q,quit,..,exit,r,reset}
options: error: argument cmd: invalid choice: 'disp' (choose from 'calc', 'yf', 'tr', 'info', 'pcr', 'load', 'exp', 'vol', 'voi', 'oi', 'hist', 'chains', 'grhist', 'unu', 'plot', 'parity', 'binom', 'vsurf', 'greeks', 'payoff', 'pricing', 'screen', 'cls', 'home', 'h', '?', 'help', 'q', 'quit', '..', 'exit', 'r', 'reset')

The command 'disp' doesn't exist on the /stocks/options/ menu.

2022 Feb 16, 08:49 (✨) /stocks/options/ $

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Co-authored-by: Jeroen Bouma <jeroenbouma@MacBook-Pro-van-Jeroen.local>
Co-authored-by: jmaslek <jmaslek11@gmail.com>
2022-02-20 13:40:58 +00:00

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usage: infer [-l LIMIT] [-h] [--export {csv,json,xlsx}]

Displays a 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: https://Twitter.com

optional arguments:
  -l LIMIT, --limit LIMIT
                        limit of latest tweets to infer from. (default: 100)
  -h, --help            show this help message (default: False)
  --export {csv,json,xlsx,png,jpg,pdf,svg}
                        Export raw data into csv, json, xlsx (default: )

Example:

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