From Comments to Trends: Studi NLP dan Computational Linguistics dalam Mendeteksi Topik Viral dari…

2026-03-11 · Source: Naturallanguageprocessing on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Computational Linguistics · Depth: Intermediate, long

Summary

The "Trending Topic Detector" system leverages Natural Language Processing (NLP) and computational linguistics to identify widely discussed topics from social media comment sections, specifically YouTube and TikTok. This system processes millions of comments daily, addressing the challenge of manually analyzing vast text data. It employs an NLP pipeline encompassing data collection, preprocessing (cleaning, tokenization, stopword removal, normalization, stemming using PySastrawi for Indonesian), word embedding, machine learning modeling (SVM, Naive Bayes, Transformer), topic detection (BERTopic, LDA), and ranking based on volume and growth rate. The article details NLP's evolution from rule-based to pre-training/fine-tuning approaches, its core tasks like text classification, sentiment analysis, and NER, and the critical role of computational linguistics in understanding language structure beyond mere word counting. It also highlights significant challenges, including language ambiguity, contextual meaning, informal language, multilingualism, low-resource languages, and ethical concerns like bias, data privacy, and disinformation amplification.

Key takeaway

For AI scientists developing social media analytics tools, understanding the interplay between NLP tasks and computational linguistics is vital. You should prioritize robust preprocessing for informal, multilingual text and integrate contextual embedding models like IndoBERT to handle linguistic nuances. Be prepared to address ethical challenges such as data bias and privacy, ensuring your system is inclusive and resistant to manipulation like astroturfing, which can distort public attention.

Key insights

NLP and computational linguistics are crucial for detecting social media trends from vast, informal, and diverse comment data.

Principles

• Data quality dictates NLP model performance.
• Contextual embeddings improve word meaning accuracy.
• Responsible NLP requires bias audits and privacy policies.

Method

The Trending Topic Detector uses an NLP pipeline: collect comments from YouTube/TikTok APIs, preprocess text, convert to word embeddings, apply ML/DL models for topic classification/clustering, then rank topics by volume and growth rate.

In practice

• Use YouTube Data API v3 and TikTok Research API for comment data.
• Implement PySastrawi for Indonesian text preprocessing.
• Employ BERTopic or LDA for unsupervised topic clustering.

Topics

• Trending Topic Detection
• Natural Language Processing
• Computational Linguistics
• Informal Language Processing
• AI Ethics

Best for: AI Scientist, NLP Engineer, AI Student, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Naturallanguageprocessing on Medium.