Social media data refers to the information that is generated and shared on social media platforms, such as Facebook, Twitter, and Instagram. This data can include things like user-generated content, such as posts, comments, and likes; metadata, such as the time a post was made and the location it was made from; and user information, such as demographic details and connections to other users. Social media data is interesting for machine learning because it is often very large and diverse, and it can be used to train algorithms to perform a wide range of tasks. For example, social media data can be used to train algorithms to identify and classify different types of content, such as images, videos, and text. It can also be used to train algorithms to understand natural language and extract meaning from text. Additionally, social media data is often generated in real-time, which means that machine learning algorithms trained on this data can be used to make predictions or take action in near-real-time.
This tutorial develops a Twitter bot in Python that will generate automated trading signals. The bot will pull real-time price data on various cryptocurrencies (Bitcoin, Ethereum, Doge, etc.) from the crypto exchange Gate.io and analyze it using predefined rules. Whenever the bot detects a relevant price change, it automatically posts a tweet via Twitter. Simple … Read more
In a previous article, we have shown how to retrieve social media data via the Twitter API in Python. However, we can do many more cool things with the Twitter API. Another cool thing is interacting with the Twitter user account and posting automated tweets. This article shows how this works. We will use the … Read more
Twitter is a rich source of data that can be used to understand current and future trends. Because tweets often include hashtags, they can be easily linked to specific contexts such as political discussions or financial instruments. This makes Twitter a valuable tool for collecting and analyzing data. In this article, we’ll demonstrate how to … Read more
Are you ready to learn about the exciting world of social media sentiment analysis using Python? In this article, we’ll dive into how companies are leveraging machine learning to extract insights from Twitter comments, and how you can do the same. By comparing two popular classification models – Naive Bayes and Logistic Regression – we’ll … Read more
