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Machine learning, a trending topic in the tech industry, is often misunderstood as merely a tool for classification or regression. However, when combined with graph theory, machine learning can be a potent instrument for extracting valuable insights, driving business growth, validating use cases, and even creating new products.
In this article, we will guide you through an intriguing exploration where we will not only predict the sentiment of tweets but also scrutinize the social network to identify key influencers. This type of analysis can be incredibly beneficial for businesses, particularly in devising marketing strategies.
We are utilizing a tweet sentiment classification dataset (https://www.kaggle.com/datasets/kazanova/sentiment140) with the following metadata:
- Target: The sentiment of the tweet (0 = negative, 2 = neutral, 4 = positive)
- IDs: The unique identifier of the tweet (2087)
- Date: The timestamp of the tweet (Sat May 16 23:58:44 UTC 2009)
- Flag: The query (lyx). If there is no query, then this value is NO_QUERY.
- User: The Twitter handle that tweeted (robotickilldozr)
- Text: The content of the tweet (Lyx is cool)
Initially, we use PySpark for data processing. We apply the necessary transformations and feature engineering to prepare our dataset. Subsequently, we train a logistic regression model for sentiment analysis.