Satirate

Historical composite sentiment score for memes posted and the histogram showing distribution of composite sentiment score among all memes.
Entity Frequency Graph from memes with positive sentiments and those with negative sentiments
Gallery of the most favourable and the least favourable memes in the last 3 months on Reddit.

Inspiration

Memes are becoming increasingly popular among teenagers and young adults, providing a critical source of sentiment information the age groups that are otherwise less expressive of their thoughts. We seek to analyse and provide useful business insights from popular memes that reach thousands of people.

What it does

Satirate systematically analyses new memes for sentiment scores and topics that are being discussed in the memes, so to provide valuable feedback to companies/organisations involved in the discussions.

How we built it

Our project is supported by Google Cloud API: Vision, Natural Language and AutoML APIs for sentiment analysis, OCR extracting of text from images and training of supervised ML models to determine custom meme formats baseline sentiment score.

Our backend is supported by SQLite3 and front end written in Plotly-Dash, an analytical dashboard framework in Python.

Challenges we ran into

Our largest hurdle was collaboration. Coming from an academic background, we learnt how to code in a vacuum. But Yhack showed us how important integration is in the real world. We learnt to seek help from mentors and to work together with each other to produce the product that we are proud of.

Accomplishments that we're proud of

We are able to successfully produce a real world analysis tool that involves supervised machine learning and computer vision to reveal critical insights about consumer sentiments.

What we learned

We have learnt to be flexible in terms of the tools we use. Learning to use Google APIs showed us a new set of great tools that we could use in our future projects. Always communicate with other competitors to exchange ideas and skills.