A Step-by-Step Guide to Building a YouTube Video Comments Responder
How to analyze comment sentiment and react automatically using AI Today, we're digging into a crucial topic for anyone who’s creating content for YouTube: How to analyze and respond to video comments quickly, using the sentiment in those comments. This tutorial is also handy for anyone who's running a YouTube channel and wants to grow engagement. Who cares about comments? We all know that the comments section on YouTube is where real magic happens.
yt_sentiment_classifier PREDICT sentiment USING engine='openai_engine', model_name='gpt-4', prompt_template='describe the sentiment of the comments strictly as "positive", "spam", or "negative". "I love this video":positive "It is not a helpful content":negative "{{comment}}.":'; This model takes the {{comment}} value as input and gives the sentiment value as output.
clause. Finally, we define the channel_id value in the WHERE clause. The condition that uses the LAST keyword on the published_at column enables you to fetch only the recently added comments. Now that the sentiment of comments is analyzed and replies are generated, let’s add some automation. Step 4. Post replies to positive comments This is the first component of the job that we’ll create in Step 7.
yt_slack.channels VALUES; Here is how you can send notifications to Slack about recent comments whose sentiment has been classified as spam: comment to the channel, so that it can fulfill the LAST keyword condition.) yt_slack.channels VALUES; Here is how you can send notifications to Slack about recent comments whose sentiment has been classified as negative: comment to the channel, so that it can fulfill the LAST keyword condition.)
psql_datasource WITH ENGINE='postgres', PARAMETERS={ "host": "host-name-or-ip-address", "port": 5432, "database": "database_name", "schema": "schema_name", "user": "postgres", "password": "password" }; To create a table and insert into it the output of the query with LAST, use this command: CREATE OR REPLACE TABLE psql_datasource.
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