用BERT分析伊朗用户对加密货币的推文情绪，准确率达83.5%
Sentiment Analysis in Twitter Social Network Centered on Cryptocurrencies Using Machine Learning
- 结合BOW、FastText与BERT等模型进行文本向量化
- BERT在中文推文情感分类中达到83.5%准确率
- 为经济管理者提供公众情绪实时洞察
加密货币是基于区块链技术的数字资产，其去中心化特性对传统金融体系和社会产生深远影响。为理解公众对加密货币的态度，本文聚焦伊朗用户在推特上的讨论，构建情感分类模型。采用自然语言处理技术如词袋模型（BOW）和FastText进行文本向量化，结合经典机器学习算法（KNN、SVM、Adaboost）与深度学习模型（LSTM、BERT）进行分类。实验结果表明，BERT模型表现最优，整体准确率达到83.50%。该研究可帮助经济管理者快速获取公众情绪，辅助政策制定与风险管控。
原文摘要 · Abstract (English)
Cryptocurrency is a digital currency that uses blockchain technology with secure encryption. Due to the decentralization of these currencies, traditional monetary systems and the capital market of each they, can influence a society. Therefore, due to the importance of the issue, the need to understand public opinion and analyze people's opinions in this regard increases. To understand the opinions and views of people about different topics, you can take help from social networks because they are a rich source of opinions. The Twitter social network is one of the main platforms where users discuss various topics, therefore, in the shortest time and with the lowest cost, the opinion of the community can be measured on this social network. Twitter Sentiment Analysis (TSA) is a field that analyzes the sentiment expressed in tweets. Considering that most of TSA's research efforts on cryptocurrencies are focused on English language, the purpose of this paper is to investigate the opinions of Iranian users on the Twitter social network about cryptocurrencies and provide the best model for classifying tweets based on sentiment. In the case of automatic analysis of tweets, managers and officials in the field of economy can gain knowledge from the general public's point of view about this issue and use the information obtained in order to properly manage this phenomenon. For this purpose, in this paper, in order to build emotion classification models, natural language processing techniques such as bag of words (BOW) and FastText for text vectorization and classical machine learning algorithms including KNN, SVM and Adaboost learning methods Deep including LSTM and BERT model were used for classification, and finally BERT linguistic model had the best accuracy with 83.50%.
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