  Sentiment Analysis is an important algorithm in Natural Language Processing
which is used to detect sentiment within some text. In our project, we had
chosen to work on analyzing reviews of various drugs which have been reviewed
in form of texts and have also been given a rating on a scale from 1-10. We had
obtained this data set from the UCI machine learning repository which had 2
data sets: train and test (split as 75-25\%). We had split the number rating
for the drug into three classes in general: positive (7-10), negative (1-4) or
neutral(4-7). There are multiple reviews for the drugs that belong to a similar
condition and we decided to investigate how the reviews for different
conditions use different words impact the ratings of the drugs. Our intention
was mainly to implement supervised machine learning classification algorithms
that predict the class of the rating using the textual review. We had primarily
implemented different embeddings such as Term Frequency Inverse Document
Frequency (TFIDF) and the Count Vectors (CV). We had trained models on the most
popular conditions such as "Birth Control", "Depression" and "Pain" within the
data set and obtained good results while predicting the test data sets.
