In this blog, we will learn what bots are and how they can skew the sentiment analysis used in your trading strategy. We will cover the following topics:
When we perform trading on the basis of market sentiment, we need to fetch data from news sources such as Twitter, Reuters, Bloomberg and Webhosie, etc. Although reading complete articles and gauging their sentiment can be difficult, estimating the sentiment of a tweet is not that complicated.
But before you estimate the sentiment of a tweet you need to know if the tweet was an automated response of a bot or made by a human.
You may ask why this is relevant?
Why should we identify a bot?
It is relevant because you need to know what the bots are doing, which in turn will tell you how the sentiment of a particular stock on Twitter is being manipulated. When we calculate the Twitter sentiment of a particular stock, we identify and remove those tweets made by bot users. This will give the true sentiment sans manipulation. This true sentiment can be a very powerful metric, when used with other technical indicators, to call the tops and bottoms of a trend.
In python, we use the library called botometer to know if a particular tweet was made by a bot or not.
The botometer library uses a machine learning algorithm trained on tens of thousands of labelled data. This algorithm’s output is a probability on a scale of 0 to 1, where 1 indicates that a Twitter account is managed by a bot.
The Botometer API takes the user id as the input and then extracts 1200 features related to that user to compute a score. The Botometer gives separate scores for the following categories:
- Network features
- User features
- Friends features
- Temporal features
- Content features
- Sentiment features
Let us discuss some of these features.
Network features of a user include information on the retweets, mentions, and hashtags that a user tweeted in the past.
For example, If the user is retweeting only those tweets made by a particular handle, then the user is most likely a bot.
This contains user-specific information such as the user name, language, location, account created date, etc., Generally, bots do not contain such information. And if they do, it will be something gibberish.
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