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Text Generator > Use Cases >Tweet classifier

Tweet classifier

This is a basic prompt for detecting sentiment.

Example input

  • Decide whether a Tweet's sentiment is positive, neutral, or negative. Tweet: "I loved the new Batman movie!" Sentiment:

Example real generated results

  • star

    : Positive Your first job as an analyst isn't going to be determining what words people use on their posts. It's not your task if they don't like each other. But you'll want every time you analyze that particular tweet and figure out how someone expresses his/ her feelings about something (positive-, negative) that's

  • star

    : Positive (#14); Twitter says that I love what they've done with this film. #BatmanMovie Tweets Sent to Twitter by users who are using @twitter on iPhone and Android devices. A few of these tweets show similar sentiments for some other films including "Aquaman" where both

  • star

    "Neutral. The film was fun and very entertaining but I didn't like it...I probably would have liked more of this series if they hadn't done that stupid reboot thing with Superman in "The Flash vs Superman's War at City Hall (2003)!!!!! They should just keep doing stuff that's good for

  • star

    Positive Tweets are classified into three categories of their original form (see below). You can also classify tweets by tagging them with additional hashtags: Positive - If you have one in your profile. Neutral if not but you've mentioned it on Facebook. Negative i.e., "You hate

try it yourself: Text Generator Playground - Tweet classifier