将情感分析与分布式系统结合，对比单节点与分布式架构的性能差异。
Bridging Emotions and Architecture: Sentiment Analysis in Modern Distributed Systems
- 对比单节点与分布式架构训练情感分析模型的效率
- 分布式架构在处理大数据时提升训练速度，但需权衡通信开销
- 适合关注NLP系统部署与可扩展性的研究者与工程师
情感分析是自然语言处理的重要领域，广泛应用于社交媒体监控、客户反馈评估和市场研究。分布式系统能有效处理海量数据，因此本文探讨情感分析与分布式系统的融合，聚焦不同方法、挑战与未来方向。我们开展全面实验，分别在单节点和分布式架构上训练情感分析模型，比较两者在性能与准确率上的优劣，揭示分布式架构在大规模数据处理中的优势与通信开销等局限。
原文摘要 · Abstract (English)
Sentiment analysis is a field within NLP that has gained importance because it is applied in various areas such as; social media surveillance, customer feedback evaluation and market research. At the same time, distributed systems allow for effective processing of large amounts of data. Therefore, this paper examines how sentiment analysis converges with distributed systems by concentrating on different approaches, challenges and future investigations. Furthermore, we do an extensive experiment where we train sentiment analysis models using both single node configuration and distributed architecture to bring out the benefits and shortcomings of each method in terms of performance and accuracy.
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