Knowledge-Based Classification Algorithms Knowledge-Based Classification Algorithms Knowledge-based classification algorithms refer to a category of machine learning algorithms that employ pre-existing knowledge or rules to classify novel instances or data points. The algorithms in question are dependent on a knowledge base, which is a compilation of established patterns, rules, or associations among attributes, for the purpose of predicting outcomes or assigning class designations to unobserved data. In contrast to conventional classification algorithms that acquire patterns or rules directly from the training data, knowledge-based classification algorithms utilise pre-existing knowledge or domain expertise to steer the classification procedure. Pre-existing knowledge can be sourced from human experts or obtained from pre-existing resources such as databases, ontologies, or expe...
Basic Introduction to Sentic Computing: Sentic computing is an interdisciplinary field that combines affective computing (emotions and feelings) and commonsense computing to analyze sentiments and opinions on the web effectively. Its goal is to enhance the recognition, interpretation, and processing of sentiments by leveraging computer science and social science techniques. Key Models and Resources: a) The Hourglass of Emotions: The Hourglass of Emotions is a popular model used in sentic computing. It represents emotions as a hierarchy, ranging from basic emotions (e.g., joy, anger) to complex emotions (e.g., love, guilt). This model helps in understanding the relationships and transitions between different emotional states. b) Sentic Patterns: Sentic Patterns are linguistic patterns or templates that capture the expression of sentiments in text.They are useful for sentiment analysis as they provide a way to identify and extract sentiment-related information from text. For example, a p...