SRS Toxic Comment Classification using Machine Learning

Toxic comments are defined as a rude or disrespectful comments that can cause other users to leave the conversation. As we live in an age of technology where most of us have easy access to the Internet. Due to the increasing use of the Internet, the use of social media, especially for communication, has increased dramatically in recent years. But this advancement also opens the door to trolls who poison social media and forums by their abusing behaviour toward other. Therefore, detection of toxic language online is becoming a major issue. In this project, student will classify toxic comments and find accuracy by applying appropriate machine learning techniques (such as SVM, Tree and Random, etc.) to toxic comment datasets. Student will also compare which technique is best for toxic comment classification and why.

Scope of Project Toxic Comment Classification using Machine Learning:

Project scope is the part of project planning that involves determining and documenting a list of specific project  goals, deliverables, tasks, costs and deadlines. Defining the project scope involves adopting a clear vision and an agreement on the outcomes of the project. This allows each milestone of the project to stay on target

Functional and Non Functional Requirements of Toxic Comment Classification using Machine Learning:

Functional Requirements of Toxic Comment Classification using Machine Learning:

A functional requirement shows that what the system must do what services the system present to users. It describes a software system or its component. A function is nothing but inputs to the software system, its behavior, and outputs

NON-FUNCTIONAL REQUIREMENTS Toxic Comment Classification using Machine Learning:

• Application is user friendly.
• Application Perform fast manipulation and calculations.
• Application is adaptable.
• Application will be able to work on all types of operating systems.
• Application will be capable to handle multi user activities simultaneously.
• There will be back up system to face any problem in system
• All the options should be learning friendly I.e. member could easily understand what that option will do if he clicked on it.
• Response Time is very awesome.

Some others are:

• Accessibility
• Maintainability
•  Fault Tolerance.
• Security
• Robustnes

Use Case Diagram of Implementation of Toxic Comment Classification using Machine Learning

a use case diagram can summarize the details of your system’s users   and their interactions with the system. Scenarios in which your system or application interacts with organizations, people, or external systems. Goals that your system or application helps those entities achieve

Usage Scenarios Implementation of Toxic Comment Classification using Machine Learning:

A brief user story explaining who is using the system and what they are trying to accomplish. A Scenario is made up of a number of simple, discrete steps that are designated as being performed by either the System or a  User.

ADOPTED METHODOLOGY for Toxic Comment Classification using Machine Learning:

The adopted methodology for this project is vu process model. Vu process model is a combination of water-fall model and spiral model. This combination has many advantages. This model has high risk analysis so avoidance of risk would be achieved. This model is easy to understand and use. Now first we will discuss the Water-fall model.

Work Plan of Toxic Comment Classification using Machine Learning:

Work plan

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