Abstract:
Google Play Store is basically an app store from where we get various kinds of applications
for our android certified devices which makes our life a lot easier and faster through the
diverse functionalities the apps contain. Numerous users are using applications as per their
needs and putting their experience, thoughts of using that application via reviews in form
of ratings and texts. As the safety of women is threatened, whether or not applications like
women’s safety apps are appreciated, can be detected through text reviews and ratings by
the users. In this study, we try to analyze the polarity (positive, negative, neutral) of the
sentences or text reviews that are given by the users of the women’s safety app through the
google play store. To detect the emotions of the users through the given text reviews and
star ratings, different machine learning (ML) and deep learning methods using natural
language processing (NLP) are conducted to analyze the sentiments of the review given by
the users. For this study, we have collected data from the app reviews and star ratings
provided by the users of the women’s safety related applications whose main purpose is to
provide necessary functionality that can keep women safe in any dangerous and unwanted
situation. The purpose of this paper is to mine the opinion of the users and get their
viewpoint about those apps of specific purpose whether it is positive, negative, or neutral.
As the current user’s ratings, reviews, or as a whole their viewpoint helps the new user
understand the performance of the applications and insights in advance, so the mining of
their opinion is helpful for both parties - developers and general users. To detect the level
of the sentiment, we have applied several supervised machine learning algorithms namely
Multinomial Naive Bayes (MNB), Logistic Regression (LR), Support Vector Machine
(SVM), and k-nearest neighbor (K-NN) as well as unsupervised deep learning algorithm
Bidirectional Encoder Representations from Transformers (BERT). Among these
algorithms, the BERT has outperformed all other algorithms in terms of accuracy
(86.06%).
