An opinionated sentiment analysis using a rule-based method

Abstract

The categorization of opinions into positive, negative, or neutral facilitates information gathering, pinpointing individual weaknesses, and streamlining the decision-making process. Precision in opinion classification enables decision-makers to extract valuable insights, make well-informed decisions, and execute suitable actions. Sentiment analysis is language-specific due to the distinct morphological structures unique to each language, distinguishing them from one another. This study implemented a rule-based sentiment analysis approach for Kafi-noonoo opinionated texts, leveraging a rule-based system tailored for smaller datasets that operate based on a predefined set of rules. The rule-based mechanism calculates the overall polarity of a given sentence by applying a set of rules and categorizes it into positive, negative, or neutral sentiments upon identifying sentimental terms from a dedicated file. While the analysis utilized 1,500 words sourced from Facebook and music review samples, the modest sample size yielded satisfactory results. Performance evaluation metrics such as precision, recall, and F-measure were employed, indicating positive word scores of 91%, 86%, and 88.4%, and negative word scores of 80%, 75%, and 77%, respectively.