用多语言模型集成提升跨语种推文情感分析准确率
Ensembling Multilingual Transformers for Robust Sentiment Analysis of Tweets
- 构建多语言Transformer集成模型，融合BERT与XLM-R
- 跨语言情感分析准确率达86%以上，无需额外标注数据
- 适合无标注数据的低资源语言情感分析场景
情感分析是自然语言处理中识别文本极性的关键任务，广泛应用于营销、政治和客户服务等领域。随着社交媒体发展，该任务的重要性日益凸显。然而，当面对非英语等外语时，尤其缺乏标注数据的情况下，情感分析性能显著下降。本文提出一种基于预训练模型的集成方法，结合bert-base-multilingual-uncased-sentiment与XLM-R，在多语言数据集上进行情感分析。实验结果表明，所提方法在跨语言场景下性能超过86%，有效缓解了低资源语言标注数据不足的问题。
原文摘要 · Abstract (English)
Sentiment analysis is a very important natural language processing activity in which one identifies the polarity of a text, whether it conveys positive, negative, or neutral sentiment. Along with the growth of social media and the Internet, the significance of sentiment analysis has grown across numerous industries such as marketing, politics, and customer service. Sentiment analysis is flawed, however, when applied to foreign languages, particularly when there is no labelled data to train models upon. In this study, we present a transformer ensemble model and a large language model (LLM) that employs sentiment analysis of other languages. We used multi languages dataset. Sentiment was then assessed for sentences using an ensemble of pre-trained sentiment analysis models: bert-base-multilingual-uncased-sentiment, and XLM-R. Our experimental results indicated that sentiment analysis performance was more than 86% using the proposed method.
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