# Perceiving University Student's Opinions from Google App Reviews

arXiv: https://arxiv.org/abs/2312.06705 · HTML: https://mlsanity.com/paper/2312.06705/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 86 references that are in the map.
- First posted on arXiv 10 Dec 2023.

## Builds on (most cited first, 7 of 7)

- [Deep Learning for Sentiment Analysis : A Survey](https://mlsanity.com/paper/1801.07883/) (2018)
- [A C-LSTM Neural Network for Text Classification](https://mlsanity.com/paper/1511.08630/) (2015)
- [Sentiment of Emojis](https://mlsanity.com/paper/1509.07761/) (2015)
- [Twitter Sentiment Analysis: Lexicon Method, Machine Learning Method and Their Combination](https://mlsanity.com/paper/1507.00955/) (2015)
- [Deep Learning versus Traditional Classifiers on Vietnamese Students' Feedback Corpus](https://mlsanity.com/paper/1911.07223/) (2019)
- [Comparative Sentiment Analysis of App Reviews](https://mlsanity.com/paper/2006.09739/) (2020)
- [Explore Recurrent Neural Network for PUE Attack Detection in Practical CRN Models](https://mlsanity.com/paper/1805.00428/) (2018)

## Abstract (arXiv)

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%).