NLP Data Labeler: Sentiment & Topic Modeling (Self-Directed Project)
OtherTextClassification
I performed NLP tasks on movie review datasets, focusing on sentiment and topic modeling using text data. My work involved applying Naive Bayes Classification and Latent Dirichlet Allocation (LDA) to label sentiment and topics within the dataset. The primary goal was to accurately classify and group text data for analytical purposes.
• Implemented supervised and unsupervised machine learning algorithms for label creation.
• Conducted sentiment analysis to categorize movie reviews as positive, neutral, or negative.
• Utilized topic modeling to identify themes across large collections of reviews.
• Presentations and reporting delivered to stakeholders showcasing results and insights.
I performed NLP tasks on movie review datasets, focusing on sentiment and topic modeling using text data. My work involved applying Naive Bayes Classification and Latent Dirichlet Allocation (LDA) to label sentiment and topics within the dataset. The primary goal was to accurately classify and group text data for analytical purposes. • Implemented supervised and unsupervised machine learning algorithms for label creation. • Conducted sentiment analysis to categorize movie reviews as positive, neutral, or negative. • Utilized topic modeling to identify themes across large collections of reviews. • Presentations and reporting delivered to stakeholders showcasing results and insights.
Not specified