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Here we are making a predictive system to measure the sentiment of each review or tweet, whether it is 1 (Positive Sentiment) or 0 (Negative Sentiment). In this work, LGBM Classifier, XGBooost Classifier, CatBoost Classifier, Random Forest Classifier, Gradient Boosting Classifier, K-Nearest Neighbors, and Logistic Regression are used.

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ankushmallick1100/Sentiment-Analysis-for-drug-reviews-tweets

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Sentiment Analysis for drug reviews tweets

Description

Here we are making a predictive system to measure the sentiment of each review tweet, whether it is 1 (Positive Sentiment) or 0 (Negative Sentiment). In this work, seven difference machine learning models - LGBM Classifier, XGBooost Classifier, CatBoost Classifier, Random Forest Classifier, Gradient Boosting Classifier, K-Nearest Neighbors and Logisitc Regression are used.

LGBM Classifier gives the best accuracy of 90.78

Dataset

Dataset is present in UCL Machine Learning Repository
Link: https://archive.ics.uci.edu/dataset/462/drug+review+dataset+drugs+com

About

Here we are making a predictive system to measure the sentiment of each review or tweet, whether it is 1 (Positive Sentiment) or 0 (Negative Sentiment). In this work, LGBM Classifier, XGBooost Classifier, CatBoost Classifier, Random Forest Classifier, Gradient Boosting Classifier, K-Nearest Neighbors, and Logistic Regression are used.

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