International Journal of Machine Learning, AI & Data Science Evolution

E-ISSN: 3067-5073

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Volume 2 Issue 5
September-October 2026

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Natural Language Processing Techniques for Automated Sentiment Classification in Online Communication

Author(s) Frederik Jensen
Country Denmark
Abstract Online communication platforms generate substantial amounts of textual information through public discussions, social media messages, user reviews, and interactive digital conversations. These communication records contain expressions of satisfaction, dissatisfaction, uncertainty, and personal evaluation that can provide useful information about collective attitudes and individual responses. However, identifying sentiment within informal language presents significant analytical difficulties because online messages frequently contain abbreviated expressions, grammatical inconsistencies, sarcasm, ambiguous terminology, and context-dependent meanings. Natural language processing techniques provide computational methods for examining such textual information and assigning sentiment categories through structured linguistic analysis. The present paper develops a hypothetical methodological framework for investigating automated sentiment classification in English-language online communication. The proposed framework incorporates text preprocessing, lexical feature extraction, supervised machine learning, and neural network classification. Four analytical approaches are considered: lexicon-based classification, support vector machines using term frequency–inverse document frequency features, convolutional neural networks, and bidirectional long short-term memory networks.
A fictional collection of online messages is introduced to illustrate the proposed experimental procedure. The framework evaluates three sentiment categories: positive, negative, and neutral. Two hypothetical analytical tables describe the intended class distribution and comparative classification performance. Accuracy, macro-averaged precision, macro-averaged recall, and macro-averaged F1-score are considered complementary evaluation metrics. The illustrative results are manually constructed and do not represent executed computational experiments. The methodological discussion examines the influence of informal writing conventions, contextual interpretation, training data quality, class imbalance, and linguistic variation on automated sentiment classification. The paper concludes that natural language processing provides a useful methodological foundation for analyzing online communication, although reliable implementation requires representative datasets, consistent annotation procedures, independent evaluation, and responsible management of communication records.
Keywords Natural Language Processing, Sentiment Analysis, Online Communication, Text Classification, Machine Learning, Convolutional Neural Networks, Support Vector Machines, Opinion Mining, Language Processing, Sentiment Detection.
Field Engineering
Published In Volume 1, Issue 2, May-June 2025
Published On 2025-05-20
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E-ISSN: 3067-5073
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