The team aims to develop a fake news detection system using sentiment analysis and machine learning. They have created a GitHub repo, collected a diverse dataset, and implemented preprocessing techniques. The system analyzes sentiment to categorize text as positive, negative, or neutral to identify potential fake news. The team plans to refine the model, address challenges like ambiguity, and develop a real-time analysis and improved user interface. Members are assigned roles like front-end development, back-end development, data science, testing, integration, and UX design.

1. Team Name: Trishul
Date: 05/06/2023

2. INTRODUCTION:
Project Objectives:
• Develop a robust system for
detecting fake news using
sentiment analysis
techniques.
• Enhance the accuracy and
efficiency of fake news
identification to combat
misinformation.
• Provide a reliable tool for
users to evaluate the
credibility of news sources
and content.

3. Recap of Current Progress and Architecture
Our project aims to develop a sentiment analysis system for fake news detection, utilizing Natural Language Processing techniques and
Machine Learning algorithms.
We have made significant progress in the following areas:
• Created Github Repo: We have created GitHub repository for our project
• Data collection: Gathered a diverse dataset of news articles and social media posts and upload on GitHub.
• Preprocessing: Implemented robust preprocessing techniques to clean and prepare the text data for analysis.
Architecture Overview:
• The system receives input text data and applies the sentiment analysis algorithm.
• The algorithm analyzes the sentiment of the text, categorizing it as positive, negative, or neutral.
• The system provides an output indicating the sentiment of the input text, aiding in the identification of potential fake news.
Challenges:
• Handling the ambiguity and context-specific nature of language to improve the accuracy of sentiment analysis.
• Expanding the dataset to include a broader range of news sources and social media platforms.
• Optimizing the system's speed and efficiency to handle large-scale data analysis.

4. Plans for Coming Week
Conduct further
data analysis:
Explore additional
data sources to
expand the
diversity and
coverage of our
dataset.
Analyze the
characteristics and
patterns of fake
news articles to
enhance the
detection
algorithm.
Refine the
sentiment analysis
model:
Fine-tune the
machine learning
model using
advanced
techniques to
improve its
accuracy and
generalization
capabilities.
Address challenges
related to language
ambiguity, sarcasm,
and context-specific
sentiment analysis.
Implement real-
time analysis:
Develop a
mechanism to
process and
analyze news
content in real-
time, enabling
prompt detection
of potential fake
news.
Ensure efficient
and scalable
processing of
incoming data to
provide timely
results.
Enhance the user
interface:
Work on improving
the user
experience by
designing an
intuitive and user-
friendly interface.
Incorporate
features that allow
users to interact
with the system,
such as submitting
articles for
analysis and
providing
feedback.
Conduct
performance
evaluations:
Perform rigorous
testing and
evaluation of the
system's accuracy,
precision, recall,
and computational
efficiency.
Validate the
system's
effectiveness
through
comparative
analysis with
existing fake news
detection methods.
Prepare for
presentation and
documentation:
Consolidate the
findings and
progress made
during the week
into a
comprehensive
report.
Prepare a
detailed
presentation to
showcase our
updated system
and discuss the
latest
developments.

5. Short Demo of
Current System / Key
Aspects
Demo Steps:
1. Input: Enter a sample news article or text snippet into the
system.
2. Sentiment Analysis: The system will analyze the sentiment of
the input text, classifying it as positive, negative, or neutral.
3. Fake News Detection: Based on the sentiment analysis
results, the system will indicate the likelihood of the input text
being fake or credible.
4. Visual Output: The user interface will display the sentiment
analysis results and provide additional details, such as the
sentiment score and confidence level.
5. Interpretation: We will interpret the output and discuss how
sentiment analysis plays a role in fake news detection.
This demo highlights the functionality and potential of our system
in identifying fake news through sentiment analysis.

6. Detection:

7. Tasks Assigned and Completed by Each Team
Member
Front End Developer Back End Developer Data Science/ Machine Learning
Name Vikram Kumar Saurabh Anand, Harnoor Singh
Chawla, Harshkumar Mehta
Vaibhav Ranka, Vikram Kumar, Arushi
Khera
Role Design and develop the user
interface, ensuring an intuitive
and visually appealing user
experience.
Develop the server-side components,
implement APIs, and handle data
storage and retrieval.
Design and implement the sentiment
analysis algorithm, train and evaluate
machine learning models, and optimize
performance.
Competencies Proficient in front-end
technologies such as HTML,
CSS, and JavaScript,
familiarity with UI/UX
principles, and ability to create
responsive designs.
Strong programming skills in
languages like Python, Java, or C#,
experience with web frameworks (e.g.,
Django, Flask), and proficiency in
working with databases.
Deep understanding of NLP techniques,
experience with machine learning libraries
(e.g., TensorFlow, PyTorch), and expertise
in feature engineering and model
evaluation.

8. Tasks Assigned and Completed by Each Team
Member
Testing Integration and Middleware UX Lead
Name Saurabh Anand, Harnoor Singh Chawla,
Harshkumar Mehta
Saurabh Anand, Harshkumar
Mehta
Vikram Kumar, Arushi Khera,
Vaibhav Ranka
Role Conduct comprehensive testing to ensure
the functionality, accuracy, and reliability of
the software solution.
Manage the integration of various
system components, handle data
flow, and ensure seamless
communication between different
modules.
Conduct user research, design user
workflows, and create wireframes
and prototypes to ensure a user-
centric approach.
Competencies Proficiency in various testing
methodologies (e.g., unit testing,
integration testing, regression testing),
knowledge of testing frameworks, and
ability to identify and report bugs
effectively.
Experience with system
integration, knowledge of
middleware technologies (e.g.,
RESTful APIs, message queues),
and ability to troubleshoot
integration issues.
Strong understanding of user
experience principles, proficiency in
UX design tools (e.g., Sketch,
Adobe XD), and ability to translate
user requirements into intuitive
designs.