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README.md
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# social-stat
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# Endpoints
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## GET `/predict/{video_id}`
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Returns `pandas` DataFrame with all `commentThreads` of a `YouTube` video with emotion scores estimated by [SamLowe/roberta-base-go_emotions](https://huggingface.co/SamLowe/roberta-base-go_emotions).
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<details>
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<summary>All DataFrame columns:</summary>
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- comment_id
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- video_id
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- channel_id
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- author_display_name
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- text_original
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- text_display
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- published_at
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- updated_at
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- like_count
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- can_reply
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- total_reply_count
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- neutral
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- approval
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- annoyance
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- disapproval
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- realization
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- admiration
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- disappointment
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- excitement
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- disgust
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- confusion
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- joy
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- anger
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- optimism
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- amusement
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- gratitude
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- surprise
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- sadness
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- fear
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- curiosity
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- love
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- embarrassment
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- desire
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- caring
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- pride
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- relief
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- grief
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- remorse
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- nervousness
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</details>
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# Installation and Running
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```
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git clone https://github.com/molokhovdmitry/social-stat
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python -m pip install --upgrade pip
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pip install -r requirements.txt
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```
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# social-stat
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Streamlit web application for social network analysis.
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# Installation and Running
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```
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git clone https://github.com/molokhovdmitry/social-stat
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python -m pip install --upgrade pip
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pip install -r requirements.txt
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streamlit run src/app.py
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```
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