Predicting of Television Channel Viewership Using a Hybrid of Naïve Bayes and Support Vector Machines Algorithms

Abstract

Traditional audience measurement systems are increasingly inadequate in capturing fragmented, cross-platform television viewership, especially as unstructured social media data becomes central to understanding audience behavior. However, the lack of standardized, scalable, and computationally efficient machine learning models for processing this data presents a major computing challenge for broadcasters seeking accurate and inclusive audience insights. The main objective of the research was to develop and evaluate the performance of a hybrid of Naïve Bayes and Support Vector Machines (SVM) machine learning algorithms in predicting television viewership using sentiment analysis of Twitter data. Focusing on two Kenyan TV programs, NTV’s AM Live and KTN’s Morning Express, the study applied a five step experimental design encompassing data collection, text preprocessing, sentiment classification, and predictive modeling. Twitter data from 2018 to 2022 was collected using Python-based tools (Tweepy and snscrape). Text mining techniques including preprocessing, categorization, and feature extraction were used to prepare the data for sentiment analysis. Using TextBlob and Scikit-learn, tweets were classified into positive, neutral, and negative sentiments. These sentiment scores served as features to train and evaluate three machine learning models: Naïve Bayes, SVM, and the hybrid ensemble model. Results indicated that Naïve Bayes outperformed SVM with a test accuracy of 65% compared to 62%. However, the hybrid model, which combined both classifiers through a soft voting ensemble, achieved a superior predictive accuracy of 77%. This finding confirms that hybrid approaches are more effective in modeling viewership behavior from unstructured social media data. The study concludes that hybrid machine learning models offer a robust and scalable approach to predicting audience engagement, providing actionable insights for broadcasters and advertisers. The integration of real-time sentiment analysis into media analytics frameworks represents a promising innovation for the future of television audience measurement.