Abstract:
Sentiment analysis, as a branch of Natural Language Processing (NLP) is becoming
more useful in healthcare by helping understand patient feedback about medicines.
This study aims to improve how we evaluate drug effectiveness by combining
advanced NLP techniques and machine learning methods. We also propose creating
a Drug Recommendation System to support healthcare professionals in choosing
the right medicines. Our study takes this further by introducing five sentiment
levels: Frustrated, Bad, Neutral, Good, and Excited, based on patient ratings. We
utilized a dataset obtained from the UCI Machine Learning Repository for this
research and collected additional data to balance the dataset. The text data is cleaned
and prepared using NLP techniques such as breaking text tokenization involves
dividing or cutting the text into small pieces and eliminating punctuation and
unnecessary words, and converting words to the root or base forms (stemming and
lemmatization). For understanding text better, we used methods like Bag of Words
(BoW), Term Frequency-Inverse Document Frequency (TF-IDF), Word2Vec, and
manual feature creation. To handle uneven data and improve results collected
additional data and also we used SMOTE-SO-MAK, a technique that creates extra
samples for less common sentiment categories. Among the different machine
learning models tested, Logistic Regression (LR) gave the best results. We checked
the system’s accuracy and performance using measures like precision, recall, F1-
score, and overall accuracy. This study improves drug recommendation systems by
integrating the latest NLP, machine learning algorithms and data balancing, and
testing methods.
