## 1 Introduction  
In an information era where a large amount of data needs to be processed every day, minute and second--and the huge demand on computers with high processing speeds to outcome accurate results within nanoseconds, it is said that all approximately 2.5 quintillion bytes of data can be manually or automatically generated on a daily basis using different tools and application. Moreover, this illustrates the importance of text-mining techniques in handling and classifying data in a meaningful way. There are a variety of applications that help in classifying a string or a text, such as those used to detect user sentiments on comments or tweets and classification of an e-mail as spam. In this research,we were able to categorize data based on a given text and provide some relevant information about the category accordingly using the essential and vital tasks of natural language processing (NLP) [1]. Mobile application stores enable users to search, buy and install mobile-related apps and allow them to add their comments in the form of evaluation and reviews. Such reviews and mobile program ecosystem have plenty of information regarding the user's expectations and experience. Moreover, here occurs the importance of the role played by programmers and application store regulators who can leverage the data to better understand the audience, their needs and requirements. Browsing mobile application stores show that hundred and thousands of apps are available and implemented every day which makes it necessary to perform aggregation studies using data mining techniques in which some academic studies focuses on user testimonials and mobile program stores, in addition to studies analyzing online product reviews. In this article, we used various algorithms and text classification techniques using Android application reviews [2]. App stores--or application distribution platforms--allow consumers to search, buy and set up applications. These platforms also enable users to discuss their opinions about an application in text testimonials, where they can, e.g., highlight a good feature in a specific application or request for a new feature [3]. Recent studies have revealed that program shop reviews include useful information for analysts. This feedback represents the \"voice of the users\" and can be employed to drive the growth effort and enhance upcoming releases of the application [4]. In this research, we cover the below main points:  
1. We scraped recent Android application reviews by using the scraping technique, i.e., Beautiful Soup 4 (bs4), request, regular expression(re);
2. We scraped raw data from the Google Play Store, collected these data in chunks and normalized the dataset for our analysis;
3. We compared the accuracy of various machine-learning algorithms and found the best algorithm according to the results;
4. Algorithms can check the polarity of sentiment based on whether a review is positive, negative or neutral. We can also prove this using the word cloud corpus. This research serves our key contributions as below:  
* One of the key findings is that logistic regression performs better compared to random forest and naive Bayes multinomial to multi-class data;
* A good preprocessing affects the performance of machine learning models;
* Term frequency (TF) overall results after preprocessing were better than the term frequency/inverse document frequency (TF/IDF). Text mining--also referred to as text data--is the process for deriving data in which information is derived via patterns inventing of and trends along with pattern learning [5]. Text mining requires simplifying the input text through parsing--typically, along with the accession of many derived linguistic aspects and the removal of others--and the following insertion into a database, even deriving patterns inside the structured information, and last, interpreting and analyzing the output [6]. Text mining describes a combo of meanings that is novelty and fascination.