基于机器学习的社交媒体数据情感分析用于预测消费者行为趋势
Sentiment Analysis of Social Media Data for Predicting Consumer Behavior Trends Using Machine Learning
AI总结：
本研究利用SVM、Naive Bayes、LSTM、BERT等机器学习模型对Sentiment140数据集中的汽车相关Twitter数据做情感分析预测消费趋势，BERT性能最优，还结合时序分析和NER，提出可扩展的消费者洞察生成框架。
摘要：
在技术飞速发展的时代，Twitter（X）等社交媒体平台已成为收集消费者洞察、捕捉多元观点、了解公众态度的不可或缺的工具。本研究将先进的机器学习方法应用于Twitter数据的情感分析，重点预测消费者趋势。研究使用Sentiment140数据集，以“汽车”为例检测消费者偏好的演变模式。研究采用结构化工作流对数据进行清洗和预处理以供分析。研究运用了包括支持向量机（SVM）、朴素贝叶斯（Naive Bayes）、长短期记忆（LSTM）网络以及双向编码器表征Transformer（BERT）在内的多种机器学习模型进行情感分类和趋势预测。研究采用准确率、精确率、召回率和F1值衡量模型性能，其中BERT取得了最优结果（准确率：83.48%，精确率：79.37%，召回率：90.60%，F1值：84.61%）。结果表明，LSTM和BERT能有效捕捉语言和上下文模式，提升预测准确率，并为消费者行为研究提供洞见。时间分析揭示了情感随时间的变化，命名实体识别（NER）则识别出相关术语和主题。本研究应对了反讽检测和多语言数据处理等挑战，提供了一个可扩展的框架以生成可落地的消费者洞察。
英文摘要：
In the era of rapid technological advancement, social media platforms such as Twitter (X) have emerged as indispensable tools for gathering consumer insights, capturing diverse opinions, and understanding public attitudes. This research applies advanced machine learning methods for sentiment analysis on Twitter data, with a focus on predicting consumer trends. Using the Sentiment140 dataset, the study detects evolving patterns in consumer preferences with "car" as an example. A structured workflow was used to clean and prepare data for analysis. Machine learning models, including Support Vector Machines (SVM), Naive Bayes, Long Short-Term Memory (LSTM) networks, and Bidirectional Encoder Representations from Transformers (BERT), were employed to classify sentiments and predict trends. Model performance was measured using accuracy, precision, recall, and F1 score, with BERT achieving the highest results (Accuracy: 83.48%, Precision: 79.37%, Recall: 90.60%, F1: 84.61). Results show that LSTM and BERT effectively capture linguistic and contextual patterns, improving prediction accuracy and providing insights into consumer behavior. Temporal analysis revealed sentiment shifts across time, while Named Entity Recognition (NER) identified related terms and themes. This research addresses challenges like sarcasm detection and multilingual data processing, offering a scalable framework for generating actionable consumer insights.
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