Multilingual Opinion Mining Using Machine Learning Algorithms

Abstract

Modern microblogging and social networking ecosystems serve as massive, continuous streams of public expression, generating expansive volumes of multilingual data that reflect global consumer sentiments and emotional trends. Parsing actionable insights from this cross-lingual text presents a major analytical challenge due to structural variations across different languages. To address this, this paper introduces an optimized, computationally lightweight multilingual opinion mining framework. The core architecture relies on a statistically robust Logistic Regression classifier designed to categorize user-generated text into precise Positive, Negative, or Neutral polarities. By harvesting real-time data from Twitter/X, the system implements a sequence of pipeline operations, including text sanitization, tokenization, and vocabulary normalization. These refined linguistic tokens are transformed into numeric arrays using optimized vectorization models before being processed by the predictive algorithm. Empirical testing indicates that this machine learning configuration delivers high classification accuracy across mixed-language inputs while maintaining remarkably low computational overhead, offering corporations and academic researchers an efficient alternative for scalable public feedback tracking.