Streaming Pipeliene:

1. Building Robust Streaming Data Pipeline To begin with, we extract Tweets from Twitter via a third-party API from Rapid API. We then integrate the APIs along Apache NiFi – an open-source tool facilitating easy ingestion and processing of large volumes of streaming data seamlessly; alongside integrating Kafka (a distributed streaming platform enabling high-speed real-time processing) followed by Spark Streaming (an efficient tool). Finally, MongoDB comes next since it facilitates storing & retrieving voluminous amounts of non-relational datasets.
2. Data Preprocessing & Sentiment Prediction Model Section two processes tweets through feature engineering tasks like stemming or removing stop words while converting text cases uniformly across all entries in preparation for training ML models capable of performing sentimental analysis. We train our model off labeled tweet dataset comprising positive/negative sentiments after running preprocessing steps above successfully completing them earlier on preprocessed Twitter feeds so when new unseen ones come later, they're analyzed instantly at runtime.
3. Sentiment Analysis and Visualization Lastly, section three lets us visualize aggregate sentiment results displayed via a web-based dashboard showing polarity scores ranging between neutral (-1 scale) up until strongly polarized (+1 range), providing graphical representations beyond mere pie chart/bar graph displays towards more interactive user experiences suitable enterprise-level decision making. Our goal was to build functional pipelines able to handle vast quantities of online content empowering people to understand attitudes toward different topics. This system can be used for various applications from social media tracking, and brand reputation management to market research and more – all in real-time.'