Andrew Ng Machine Learning Tweets with Sentiment Polarity Labels

8 columns include Tweet, polarity, username, and timestamp. The polarity column provides sentiment labels ranging from neutral to positive and negative. This dataset consists of tweets posted by Andrew Ng, co-founder of Coursera and adjunct professor at Stanford University.

Use Cases

• Train sentiment analysis models based on the polarity labels.
• Analyze tweet content and themes from a prominent machine learning educator.
• Study the relationship between tweet text and assigned sentiment scores.
• Perform natural language processing tasks on curated social media text.

Strengths

• Polarity column provides a 5-point sentiment scale (1-5) for each tweet.
• Data is focused on tweets from a single, authoritative source in machine learning.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Last update date is unknown; freshness unverified.

Provenance

Source

Tweets posted by Andrew Ng.

Collection Method

Likely collected from Twitter.