  Google app market captures the school of thought of users from every corner
of the globe via ratings and text reviews, in a multilinguistic arena. The
potential information from the reviews cannot be extracted manually, due to its
exponential growth. So, Sentiment analysis, by machine learning and deep
learning algorithms employing NLP, explicitly uncovers and interprets the
emotions. This study performs the sentiment classification of the app reviews
and identifies the university student's behavior towards the app market via
exploratory analysis. We applied machine learning algorithms using the TP, TF,
and TF IDF text representation scheme and evaluated its performance on Bagging,
an ensemble learning method. We used word embedding, Glove, on the deep
learning paradigms. Our model was trained on Google app reviews and tested on
Student's App Reviews(SAR). The various combinations of these algorithms were
compared amongst each other using F score and accuracy and inferences were
highlighted graphically. SVM, amongst other classifiers, gave fruitful
accuracy(93.41%), F score(89%) on bigram and TF IDF scheme. Bagging enhanced
the performance of LR and NB with accuracy of 87.88% and 86.69% and F score of
86% and 78% respectively. Overall, LSTM on Glove embedding recorded the highest
accuracy(95.2%) and F score(88%).
