%0 Journal Article %1 noauthororeditor %A Mishra, Sumit %D 2021 %J International Journal of Trend in Scientific Research and Development %K Analysis Classification Engineering Features NLP Sentiment textblob %N 4 %P 937-941 %T Drug Review Sentiment Analysis using Boosting Algorithms %U https://www.ijtsrd.comcomputer-science/artificial-intelligence/42429/drug-review-sentiment-analysis-using-boosting-algorithms/sumit-mishra %V 5 %X Sentiment Analysis of the Reviews is important to understand the positive or negative effect of some process using their reviews after the experience. In the study the sentiment analysis of the reviews of drugs given by the patients after the usage using the boosting algorithms in machine learning. The Dataset used, provides patient reviews on some specific drugs along with the conditions the patient is suffering from and a 10 star patient rating reflecting the patient satisfaction. Exploratory Data Analysis is carried out to get more insight and engineer features. Preprocessing is done to get the data ready. The sentiment of the review is given according to the rating of the drugs. To classify the reviews as positive or negative three Classification models are trained LightGBM, XGBoost, and CatBoost and the feature importance is plotted. The result shows that LGBM is the best performing Boosting algorithm with an accuracy of 88.89 . Sumit Mishra "Drug Review Sentiment Analysis using Boosting Algorithms" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42429.pdf Paper URL: https://www.ijtsrd.comcomputer-science/artificial-intelligence/42429/drug-review-sentiment-analysis-using-boosting-algorithms/sumit-mishra

@article{noauthororeditor, abstract = {Sentiment Analysis of the Reviews is important to understand the positive or negative effect of some process using their reviews after the experience. In the study the sentiment analysis of the reviews of drugs given by the patients after the usage using the boosting algorithms in machine learning. The Dataset used, provides patient reviews on some specific drugs along with the conditions the patient is suffering from and a 10 star patient rating reflecting the patient satisfaction. Exploratory Data Analysis is carried out to get more insight and engineer features. Preprocessing is done to get the data ready. The sentiment of the review is given according to the rating of the drugs. To classify the reviews as positive or negative three Classification models are trained LightGBM, XGBoost, and CatBoost and the feature importance is plotted. The result shows that LGBM is the best performing Boosting algorithm with an accuracy of 88.89 . Sumit Mishra "Drug Review Sentiment Analysis using Boosting Algorithms" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42429.pdf Paper URL: https://www.ijtsrd.comcomputer-science/artificial-intelligence/42429/drug-review-sentiment-analysis-using-boosting-algorithms/sumit-mishra }, added-at = {2021-07-15T10:47:25.000+0200}, author = {Mishra, Sumit}, biburl = {https://www.bibsonomy.org/bibtex/202cc99c0130b7a2d91052e94c5a6e2ee/ijtsrd}, interhash = {203bed42a910f6ac057c1b88888b8e3c}, intrahash = {02cc99c0130b7a2d91052e94c5a6e2ee}, issn = {2456-6470}, journal = {International Journal of Trend in Scientific Research and Development}, keywords = {Analysis Classification Engineering Features NLP Sentiment textblob}, language = {english}, month = jun, number = 4, pages = {937-941}, timestamp = {2021-07-15T10:47:25.000+0200}, title = {Drug Review Sentiment Analysis using Boosting Algorithms }, url = {https://www.ijtsrd.comcomputer-science/artificial-intelligence/42429/drug-review-sentiment-analysis-using-boosting-algorithms/sumit-mishra}, volume = 5, year = 2021 }