When Rotten Tomatoes Isn't Enough: Twitter Sentiment Analysis with DSE Part 3

Part 3 of the blog series explores the use of DataStax Enterprise Analytics and various technologies like Apache Cassandra, Apache Spark, PySpark, Python, and Jupyter Notebooks for conducting text analytics, specifically sentiment analysis on movie-related Twitter data. The process involves setting up the necessary environment, including DataStax and the Twitter Developer API, then using Apache Spark’s MLlib functions such as Tokenizer and StopWordsRemover to preprocess the tweets. The analysis is performed using the Pattern library in Python to determine sentiment scores from cleaned tweets, which are stored in Cassandra tables. By comparing positive and negative sentiment scores, the analysis concludes whether a movie is generally liked based on the Twitter data, exemplified by an analysis of "SpiderVerse," which was found to be positively rated. The blog emphasizes the iterative nature of data science and encourages further exploration and feedback.

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