Research on NLP Techniques for Analyzing Social Media User Behavior

Abstract

This paper presents a systematic review of the technical framework and notable advancements in natural language processing (NLP) technology for analyzing social media user behaviorIn the study of text feature extraction, the initial focus is on the evolution of semantic methodologies, transitioning from bag-of-words techniques to more sophisticated word vector models. It compares Word2Vec, GloVe, and FastText in terms of how well they can convey semantics, how much processing power they need, and how well they can handle words that aren’t in their lexicon. The study evaluates the limitations of coarse-grained sentiment analysis for sentiment feature extraction and examines how fine-grained methodologies, like Aspect-Based Sentiment Analysis and Emotion Cause Extraction, rectify these deficiencies through specifically delineated tasks and customised model architectures. The research analyses performance disparities in behavioural categorisation across traditional shallow models, such as SVM and LSTM, highlighting their inherent limitations, and emphasises BERT’s advantages in utilising bidirectional context to improve classification results.This systematic technical approach includes text feature extraction, sentiment analysis, and behavioral categorization, offering a reference for analyzing social media user behavior, a research methodology for related studies, and a foundation for multiple field integration.