Announcing Invitation only Data Challenge — OCEAN Twitter Sentiment Challenge
Welcome to the first invite-only data challenge hosted by Ocean Protocol. We received great feedback when tasked our data science community with sentiment analysis of the OCEAN token.
To extend this excitement, we have invited the top 6 submissions to return for an invite-only data challenge to delve deeper into the relationship between social media engagement and the price of the OCEAN token.
Let’s Compete
In this competition, we delve deeper into the relationship between social media engagement and the price of the OCEAN token. By exploring additional factors and leveraging machine learning techniques, we aim to uncover valuable insights into the complex dynamics of the cryptocurrency market.
We are excited to see how invitees identify relevant factors, source the appropriate data, conduct a thorough analysis, and present their findings in a comprehensive report. Their exceptional skills and expertise will be tested in this exclusive competition.
A tool to quantify and predict volatility in capital markets based on social media sentiment is a long-term outcome of this initiative. Starting with the OCEAN token, for example, this forefront of sentiment analysis is set to move to a broader spectrum of crypto assets, tokens, and commodities.
Both live and expired Ocean Data Challenges can be found here: https://desights.ai/o/0x8264CE39F7532871868053198fCDC2E2C81d074a
What is the task?
Participants invited to the second leg of the OCEAN token sentiment challenge will compete for a $5000 prize pool, and are tasked with the following:
- Identifying three key factors that could potentially influence the price of the OCEAN token, considering social media engagement.
- Provide a detailed description of the data sources you would consider for each identified factor. Be specific about the datasets, APIs, or platforms you would utilize to obtain the necessary data for your analysis.
- Design a methodology to measure and analyze the relationship between social media engagement and the price of the OCEAN token. Explain how you would collect and preprocess data to conduct sentiment analysis on platforms like Twitter or any other relevant social media platform.
- In addition to social media sentiment, outline their approach for exploring the impact of the external factors you identified in question 1. Describe necessary data transformations, calculations, or statistical techniques you would employ to analyze the relationships between these factors and the OCEAN token price.
- Develop a machine learning model that can provide insights into the relationship between social media engagement and the price of the OCEAN token.
- Describe the ML model you chose and explain why it suited this task. Outline the steps involved in training, evaluating, and interpreting the model’s predictions. Include details such as the choice of algorithms, feature engineering techniques, model training methodology, and any considerations for handling potential challenges, such as data imbalance or overfitting. Explain how the ML model contributed to your analysis and supported your findings in the report.
The deadline for invitees to submit responses is July 18, 2023. Reports will be made available and findings shall be shared with the community upon scoring of said entries. This is very exciting for us. We are eager to see what the brightest minds in data science and data engineering can formulate in this research!
Do you think your project could benefit from social media sentiment analysis? We encourage you to explore our community of data scientists and crypto enthusiasts and examine the benefits of running a data challenge on your protocol with Ocean Protocol Data Challenges.
About Ocean Protocol
Ocean was founded to level the playing field for AI and data. Ocean tools enable people to privately & securely publish, exchange, and consume data.
Follow Ocean on Twitter or Telegram to keep up to date. Chat directly with the Ocean community on Discord. Or, track Ocean progress directly on GitHub.
