This document describes Positive Panda, a browser extension that aims to filter out negative news and promote positive news. It uses AI to analyze news articles and determine if they are positive or negative. The extension displays a summary of the top positive news stories for the user each day. It allows users to browse news in a seamless, positive way while maintaining their privacy by not sending user data to a server. The technical description explains how Positive Panda's pipeline uses Google Cloud Platform and MongoDB Atlas for scalable hosting, with a sentiment analysis model that identifies the tone of news articles.

1. P O S I T I V E
P A N D A
A B R O W S E R E X T E N S I O N
T O H E L P Y O U R E M O V E
N E G A T I V I T Y F R O M Y O U R
D A I L Y N E W S A N D I N D U L G E
I N T H E P O S I T I V I T Y O F T H E
W O R L D

2. W H Y
P O S I T I V E
P A N D A ?

3. Why Positive Panda?
• Our AI powered solution
“Positive Panda” can tell if the
news articles on Google News
are positive or negative
• It also finds the salient
keywords from the news article
to display to the users.

4. Daily News
served right
to you
• Positive news served daily to
your doorstep.
• Users can click our extension
icon on the top to view the top
positive news from around the
world.
• This selection of news is
updated daily.
• Users can choose between
categories
Why Positive Panda?

5. All the Control
with All the
Ease
Why Positive Panda?
We provide you the ability to proceed with care.
The users are not only provided with the notable
keywords but also a rating so that they may assess
whether they want to view the news article.
We ensure user privacy is maintained by not sending
user data over a server

6. T A R G E T
A U D I E N C E

7. All
Demographics
• We aim to target people of all
demographics, as our tool
allows people to enjoy a
seamless and positive
experience while browsing the
news
• Our product aims to spread
love by limiting violent and
negative content while also
suggesting the positive ones !

8. M A R K E T
A N A L Y S I S

9. 1. Soothe
• An extension to blur out articles that
have homophobic or trans-phobic
terms
• Only works if the article specifically
has specific terms that are considered
trans-phobic or homophobic
• Does not flag articles if they are violent
or negative but do not explicitly
contain such words. Our solution
can flag these articles as well since it is
AI driven
Market Analysis

10. 2. Social Street
Smart
• More focus on detection of
fake news
• Our solution is unique as it
aims to completely filter out
violent and negative news
instead of filtering fake
news
Market Analysis

11. T E C H N I C A L
S L I D E S
F R O M H E R E , W E W I L L G O U N D E R T H E
H O O D T O D E S C R I B E T H E T E C H N I C A L
A S P E C T S O F O U R S O L U T I O N

12. W H Y G C P
A N D
M O N G O D B ?

13. Google Cloud
Platform
UsingGCP'sKubernetesEngine,GKEwe
havethe abilitytoindependentlyand
automaticallyscaleeach module with
popularityanddemand.
Scale
UsingGCP'sContainerRegistry,GCRwe
don'thaveto worryaboutimagehosting
andsecurity.
Security

14. MongoDB
Atlas
MongoDB scales with our users and
popularity because MongoDB’s
horizontal, scale-out architecture can
support huge volumes of both data and
traffic.
MongoDB Atlas makes it very easy to
have an instance running in all major
Cloud Providers,including GCP. It
removes the headache of setting up and
managing MongoDB in a secure way.

15. P I P E L I N E

16. Flow of the
Application
User visits Google News to get their news
Theextension sendsthe news articles to the backend server.
Theserver comparesfor pre-computed resultsfor the news article in the MongoDB
database. If found,the server returnsthe results,else the server queuesthe news
article on Rabbit MQ and returnsa <UNK> result.
Thequeued articles are sent to the Sentiment Analysis model runningonthe
KubernetesEngine on GCP.
Thecomputed resultis pushedonto another queuewhich is sent to the Flask server
to pushthe resultsto the database. (Change:The model sendsa post requestwith the
computed results to the Flask server to add to the database)
Once the user extension receives the results,it blursthe relevant news articles. If the
responseincludes<UNK>, it blursand displays a <UNK> tag to the user.It then sends
another request to the server after a set interval to update the <UNK> articles.

17. The Scoring
Model
The model works by building
upon the following libraries:
1. Newspaper3k: This is used for
scraping and parsing the
content of the articles.
2. Huggingface: The sentiment
Analysis model is
implementedusing the
HuggingFace library.

18. H A P P Y
S U R F I N G !