Twitter Sentiment Analysis
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Updated
Oct 27, 2024 - Jupyter Notebook
Twitter Sentiment Analysis
A flexible sentiment analysis classifier package supporting multiple pre-trained models, customizable preprocessing, visualization tools, fine-tuning capabilities, and seamless integration with pandas DataFrames.
E-Commerce Comment Classification with Logistic Regression and LDA model
Sentiment analysis on emotions dataset
🔥 Sentiment Classifier App: Instantly predict whether a review is positive or negative using machine learning! 🧠💬 Built with Logistic Regression, trained on 84K+ reviews, with 91.22% accuracy! 🚀
🔥 Sentiment Classifier App: Instantly predict whether a review is positive or negative using machine learning! 🧠💬 Built with Logistic Regression, trained on 84K+ reviews, with 88.15% accuracy! 🚀
Classic NLP Viterbi model for sentiment prediction task implemented with MIRA and SWVM algorithms
Aspect based sentiment analysis is the determination of sentiment orientation of different textual review or post based on the aspect terms associated with that review or post. After pre-processing the data, classification report is obtained for multiple ML and Neural Network Models on training data-set and the best among them is then used for c…
Sentiment detection model using many-to-one LSTMs on airline text reviews and generate contextually relevant text by training on "Alice's Adventures in Wonderland".
🔥 Sentiment Classifier App: Instantly predict whether a review is positive or negative using machine learning! 🧠💬 Built with Logistic Regression, trained on 84K+ reviews, with 91.51% accuracy! 🚀
🔥 Sentiment Classifier App: Instantly predict whether a review is positive or negative using machine learning! 🧠💬 Built with Logistic Regression, trained on 84K+ reviews, with 86.80% accuracy! 🚀
This project analyzes app reviews using sentiment classification powered by DistilBERT. It employs machine learning and natural language processing (NLP) techniques to determine sentiment as positive, neutral, or negative. The analysis provides valuable insights into user feedback for app improvement.
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