Wabra
Chat Analyzer
Sentimental and Analytical Analysis
Our company
Wabra, provides you with the best and most straightforward way to analyze your chat analytically or by sentiment analysis.
It helps you to find the feelings your chat partners are feeling.
Table of contents
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03
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Listing all the features our site provide.
Describing about the budget we need to make our project more efficient.
Listing all the libraries of python we used.
Wabra Chat Analyzer
the project
Libraries
Feature List
Market Strategy & Budget
Steps + Problem Statement + Challenges Faced + Target Audience
Team Members
Major Areas
our team
THE PROJECT
01
Wabra Chat Analyzer
What we are working on
Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so.
The process of finding trends and correlations in our data by representing it pictorially is called Data Visualization. To perform data visualization in python, we can use various python data visualization modules such as Matplotlib, Seaborn, etc
Sentiment analysis (or opinion mining) is a natural language processing (NLP) technique used to determine whether data is positive, negative or neutral. Sentiment analysis is often performed on textual data to help businesses monitor brand and product sentiment in customer feedback, and understand customer needs.
Machine learning
Data Visualization
Sentimental analysis
About the project
Wabra Chat Analyzer is an AI software solution to help organizations measure both sentiment and engagement on their chat platforms. This software takes text-based chat conversation data and finds out an individual’s emotions toward a particular topic. It also allows users to identify common words used, the frequency of conversation, and how active the person was in the conversation. This chat analyzer can be utilized for different purposes including customer service, employee satisfaction, better decision-making, and market planning. It helps businesses to gain insight from their customers by identifying their interests and feelings towards certain topics. With this data, companies can make better business decisions by understanding the customers’ needs more effectively.
Used Libraries
Pandas | Pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool |
matplotlib | Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations |
emoji | This Library is used to incorporate and use emoji in python file. |
streamlit | Streamlit is an open-source app framework for Machine Learning and Data Science teams. Create beautiful web apps in minutes. |
seaborn | Seaborn provides a high-level interface for drawing attractive and informative statistical graphics. |
urlextract | URLExtract is python class for collecting (extracting) URLs from given text based on locating TLD |
wordcloud | Word Cloud is a data visualization technique used for representing text data by the size of text. |
Re | A RegEx, or Regular Expression, is a sequence of characters that forms a search pattern. |
For more info:�+91 9548638618 | bansalaruj77@gamil.com
Major requirements (For Developers)
Python is a high-level, general-purpose programming language. Its design philosophy emphasizes code readability with the use of significant indentation.!
Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so.
Python
Machine learning
Wabra Chat Analyzer!
What are the features it provides you:
And the most important thing: Its all Free and Working
Data Visualization Sentimental Analysis
Features list
Select User
The most fantastic feature is that you can analyze the data and sentiments of a specific user and it is all decentralized, so the user's data is secured.
Market Strategy and Budget
5K
30K
25K
60,000
We want some paid API’s to make our site more accurate and efficient.
Other than these we want hosting and other budgets.
We want Amazon Web Services so that we can create a efficient environment.
api
Aws
miscellaneous
Project goals
Our goal is to make it easy to find people’s feeling based on text messages.
We want to make it easy for the corporations and people found out about the insider analytics.
Our aim is to digitalize the manual process of finding the right person.
Make Wabra the India’s first sentimental analytic website.
Goal 1
Goal 2
Goal 3
Goal 4
Predicted results
Our current percentage of data visualization is the highest.
Data visualisation 65%
Sentimental analysis 25%
The sentiments we show will be near about 25%.
Miscellaneous 10%
These are the other stats we will show.
Steps to use
Open site
Select
Drag and drop or select the chat file.
You are all set Up.
WABRA
Violah!
analysis
Click on ‘Show Analysis’ button to show the analysis.
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Open our site
Problem Statement
Chat Analyzer is an AI software solution to help organizations measure both sentiment and engagement on their chat platforms. This software takes text-based chat conversation data and finds out an individual’s emotions toward a particular topic. It also allows users to identify common words used, the frequency of conversation, and how much the active person was in the conversation.
This chat analyzer can be utilized for different purposes including customer service, employee satisfaction, better decision-making, and market planning. It helps businesses to gain insight from their customers by identifying their interests and feelings towards certain topics. With this data, companies can make better business decisions by understanding the customers’ needs more effectively.
Revenue model
Target Audience
Our target audience are both businesses and customer.
We provide a business dashboard for the businesses that will help them to analyze their personal records and data.
And for customers we will provide the analyzer which will also help them to find the sentiments of the people.
Challenges Faced
Building Chat Analyzer was a challenging task. One of the bugs we initially ran into was related to data visualization. By creating the logic to display the data in an intuitive and comprehensive way, we were able to resolve that issue.Also, because the system was connected with databases from different sources, we had to implement deployable solutions for data normalization and cleansing for accurate results. With proper debugging and testing, we were able to make sure that all components on the system functioned properly without any errors.I was successful in resolving these issues and eventually gave life to Chat Analyzer.
Our team
Aruj Bansal
Developer
Project Lead
Ajay Kumar Garg Engineering College
9548638618
bansalaruj77@gmail.com
Thanks!
Do you have any questions?
+91 95486 38618
Wabra-chat-analyzer.onrender.com
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