Adoption of a Revised Text Classification Method Using the Laravel

Framework

RAJEEV TRIPATHI

Computer Science

School of Management Sciences Lucknow

Uttar Pradesh

INDIA

Abstract: - The prevalence of the Internet of Things has increased the frequency of malicious attacks on the

many vulnerable devices, such as cellphones. Conventional framework design techniques for web design have a

lot of limits, are time-consuming, and have other problems. The industry norm for message exchange and

fusion between dissimilar systems has long been web services. We provide a machine learning-based strategy

for extracting SPAM messages based on the MVC concept and Laravel. In order to solve these problems, this

article offers a web design and implementation technique based on the Laravel framework. By automatically

handling connections that lack business logic, Laravel standardises the development process.

Key-Words: - Data Analytics, AI, Text Mining through Laravel, MVC model, Machine Learning

Received: March 9, 2024. Revised: October 12, 2024. Accepted: November 9, 2024. Published: December 11, 2024.

1 Introduction

In this age of fast software development, online

applications are gaining inexorable popularity, with

many of them serving millions of users each day

and reaching towards its peak. Monolithic

application architecture is not suited for today's

large-scale Internet applications, particularly in

dispersed and heterogeneous computer

environments, nor is it compatible with multi-team

collaborative development.

Text mining is an indexing technique that intends to

extract structured text data from unstructured text

data. The text mining process entails collecting,

preparing, and analyzing documents from a variety

of sources. When acquiring structured data from

unstructured databases, certain procedures are

followed to guarantee that users are satisfied [1].

After that, text mining techniques including

information retrieval, classification, clustering, and

categorization are employed to guarantee that the

data is appropriately processed and created [2].

The processing of structured or semi-structured data

has become highly complicated throughout all

enterprises as data volumes have raised

dramatically. TF, IDF, TF-IDF, and other

approaches or algorithms can be used to process

data. TF-IDF is a numerical statistic that displays

the relevance of keywords to certain documents, or

it can be argued that it offers those keywords that

may be used to identify or categories specific

documents [3]. Term Frequency (TF) is a metric

that measures how infrequently a term word appears

in the document. Because total document length

might range from very short to extremely long, any

phrase may appear more frequently in large papers

than in small documents [4]. To solve this problem,

the word frequency is computed by dividing the

occurrence of any term in a document by the total

number of terms in the document. IDF (Inverse

Document Frequency) provides a lower weight to

often occurring words and a higher weight to

infrequently occurring terms. The term frequency

(TF) and the inverse document frequency (IDF) are

easily multiplied [5].

International Journal of Computational and Applied Mathematics & Computer Science

DOI: 10.37394/232028.2024.4.14

Rajeev Tripathi

E-ISSN: 2769-2477

133

Volume 4, 2024

The TF-IDF method has certain drawbacks that

must be addressed. The main limitation of TF-IDF

is that it cannot detect words with minor tense

changes. For instance, the algorithm would regard

"go" and "goes" as two different autonomous words,

just as it will take "play" and "playing," "mark" and

"marking," and "year" and "years" as separate terms.

Because of this restriction, when the TF-IDF

method is used, it might occasionally provide

surprising results. Another drawback of the TF-IDF

is that it can't verify the semantics of text in

documents, thus it's only helpful up to the lexical

level. It also lacks the ability to look for word co-

occurrences. Decision Trees, Pattern or rule-based

classifiers, SVM classifiers, Neural Network

classifiers, and Bayesian classifiers are some of the

techniques that may be used to enhance

performance and accuracy.

2 Problem Formulation

2.1.1 API Technology

REST is a technological standard for Web

application that enables for heterogeneous

interoperability. The use of REST can result in an

architecture that is simple, scalable, effective, safe,

and dependable. REST is a lightweight RPC

protocol based on HTTP. Its ease of use and web

friendliness make it an unrivalled alternative to

SOAP, which has been a popular RPC solution for

years. Many developers have successfully

implemented simple and powerful APIs based on

Ajax and Restful web services utilising Rest.

2.1.2 Restful API

Representational state transfer (REST) is a method

of allowing computer systems on the Internet to

communicate with one another. Using a standard

and established set of stateless actions, REST-

compliant Web services enable requesting systems

to access and alter textual representations of Web

resources. Roy Fielding developed and defined the

term representational state transfer in his PhD

dissertation in 2000. HTTP 1.1 and Uniform

Resource Identifiers were designed using REST by

Fielding (URI). An application object, a database

record, an algorithm, and other types of information

that can be accessed are examples of resources.

REST represents URI in the form of "/user/name,"

and actions on HTTP methods GET, PUT, POST,

DELETE, HEADER, and OPTIONS result in the

next resource being delivered back to the caller. The

server side of REST maintains statelessness between

multiple encounters, meaning that any server in the

cluster can serve the client on any request [16][18].

2.2.1 Model-View-Controller (MVC)

Model-View-Controller (MVC) is a design pattern

that segregates an application into three logical

components: model, view, and controller. Each of

these components is designed to deal with a distinct

element of application development. MVC is a

popular web development framework for building

scalable and adaptable projects [6]. The MVC

framework was first used in Small Talk apps to

generate user interfaces (Programming Language).

The system is separated into three components in

this approach: the Model, which shows the Logic

area, the View, which displays the user interface,

and the Control, which handles changes to the View

[7][20]. Fig. 1. Shows the components of MVC

model.

Fig. 1. MVC Components

2.2.1 Model: All data-related functionality that the

user deals with is represented by the Model

component. This might be the data being exchanged

between the View and Controller components or

any other data pertaining to business logic. A

Customer object, for example, will get customer

data from a database, alter it, and either update it

back to the database or utilise it to render data.

2.2.2View: All of the application's UI functionality

is handled by the View component. The Customer

International Journal of Computational and Applied Mathematics & Computer Science

DOI: 10.37394/232028.2024.4.14

Rajeev Tripathi

E-ISSN: 2769-2477

134

Volume 4, 2024

view, for example, will have all of the UI

components that the final user interacts with, such

as text fields, dropdowns, and so on.

2.2.3 Controller: Controllers serve as a link

among the Model and View components, processing

all business logic and incoming requests,

manipulating data using the Model, and interacting

with Views to display the final output. The

Customer controller, for example, will handle all

interactions and inputs from the Customer View and

use the Customer Model to update the database. The

Customer data will be viewed using the same

controller.

A Model is used to interface with a database, which

need not be one. It may be a JSON file or another

type of resource. A Controller holds the logic for

dealing with a Model, such as verifying form data

and saving a resource to the database. The view is

the application’s User Interface (UI) that includes

HTML or presentation markup. Loops and

conditionals are examples of logic. Logic is

included in Views via template engines. Blade

template engine is used by Laravel to provide logic

to views [8].

3 Laravel Framework

Laravel is a free, open-source PHP web framework

applications that follow the model–view–controller

(MVC) architectural paradigm. A modular

packaging system with a dedicated dependency

management is only one of Laravel's features[10].

The laravel framework is simple to grasp and

extremely powerful. It includes authentication,

routing, session management, caching, IoC

container, and a plethora of other widely used

components, as well as fantastic database

conversion tools and integrated unit testing support.

3.1.1 Text Classification with TNTSearch

and Laravel

Online frameworks assist developers in this process

by providing a standardised method for developing

and deploying web applications. These frameworks

are frequently used to decouple the logic and the

view, i.e., to implement the Model-View-Controller

(MVC) designs [9]. Furthermore, frameworks allow

developers to reuse design and implementation by

integrating the methods for the many activities that

must be completed. These frameworks benefit the

project by reducing development time, reducing

complexity, increasing productivity, extending

extensibility, and ensuring dependability.

Laravel was designed by Taylor Otwell and is now

one of the most widely used open-source PHP web

frameworks for developing online applications. It is

built on Symfony and follows the model-view-

controller architecture paradigm. Laravel is a PHP

MVC framework that was first published in 2011. It

is one of the most well-known MVC-based PHP

frameworks, and researchers have determined that

Laravel surpasses other MVC frameworks, making

it the best PHP framework for future web

technologies [10][18]. Django is a free and open-

source web framework that is based on the Python

programming language. It was first launched in

2005 and immediately gained popularity owing to

the reusability of components; rapid development

enables sophisticated web applications to be

developed in less time [11].

It's difficult to deny that machine learning is

becoming increasingly common. Everyone is talking

about it, and it appears that everyone is using it.

True, machine learning activities require a lot of

computational power, but language speed isn't

everything. Spam Detection, Text Categorization,

Sentiment Analysis are the several applications of

Text classification [12][22]. Multinomial Naive

Bayes is a cutting-edge text categorization method.

This is a probabilistic learning approach that

determines the likelihood of a document falling into

a specific category.

4 Problem Solution

Let’s say people running a massages on their mobile

or emails and they want to classify their massages.

They want to see which massages are SPAMs and

which are not. First, I need to install TNTSearch,

which is a search engine entirely written in PHP but

International Journal of Computational and Applied Mathematics & Computer Science

DOI: 10.37394/232028.2024.4.14

Rajeev Tripathi

E-ISSN: 2769-2477

135

Volume 4, 2024

also has some cool stuff like classification [13],

which is part of information retrieval, and this is of

course also machine learning. So, installing

composer require teamtnt/tntsearch

I have created a project file named findata.php.

<?php

require _DIR_ . ‘/vendor/wholedata.php’;

use

TeamTNT\TNTSearch\Classifier\TNTClassifier;

$massages=loadCSV(‘/load/massages/from/mobile

massage.csv’);

$classifier=new TNTClassifier();

foreach ($massages as $massage)

{

$classifier->learn($massage-

>text,$massage->category)

}

$classifier->save('./path/to/usefulmassages.cls');

Using above piece of code basically I take the .csv

file containing the massages together with their

category which can be SPAM and teaches he

classifier. Finally the trained model is saved as

./path/to/usefulmassages.cls

In this way we train our model. We only need to do

the training one. For more accurate predictions we

should take large dataset as possible. Now I have a

trained model, predicting if a massage is SPAM or

not is easy. In my predict.php, I have the following:

<?php

require __DIR__ . '/vendor/wholedata.php';

use

TeamTNT\TNTSearch\Classifier\TNTClassifier;

$classifier = new TNTClassifier();

$classifier->load('./path/to/usefilmassages.cls');

$predict = $classfier->predict('This is a not spam

massage');

echo $predict['label'];

Using above code I load the model from source and

ask classifier to tell what the prediction might be.

After performing certain tests with large datasets

and the performance and accuracy are amazing. The

SPAM massages classification test has a score of

98.34753%

5 Conclusion

The notion of using the Laravel framework to

construct the fundamental infrastructures of the

Restful service is discussed in this article, as well as

essential implementation details for our approach.

Restful Web Services has become the preferred

technology model for micro services applications

due to its lightweight, scalability, and HTTP

protocol compatibility. This paper shows how to

design and construct a web application using the

Laravel framework. The development process and

automates the processing of non-business logic

relationships. It implements a basic Laravel model

that allows for automated processing of a portion of

the design through php. According to the results of

the experiments and simulations, web design based

on the Laravel framework has scalability and strong

scalability, which improves development

productivity.

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Contribution of Individual Authors to the

Creation of a Scientific Article (Ghostwriting

Policy)

The authors equally contributed in the present

research, at all stages from the formulation of the

problem to the final findings and solution.

Sources of Funding for Research Presented in a

Scientific Article or Scientific Article Itself

No funding was received for conducting this study.

Conflict of Interest

The authors have no conflicts of interest to declare

that are relevant to the content of this article.

Creative Commons Attribution License 4.0

(Attribution 4.0 International, CC BY 4.0)

This article is published under the terms of the

Creative Commons Attribution License 4.0

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International Journal of Computational and Applied Mathematics & Computer Science

DOI: 10.37394/232028.2024.4.14

Rajeev Tripathi

E-ISSN: 2769-2477

137

Volume 4, 2024