Bridging Emotions and Architecture: Sentiment Analysis in Modern Distributed Systems
This work addresses efficiency challenges in sentiment analysis for applications like social media monitoring and customer feedback, though it represents an incremental improvement by applying existing methods to distributed architectures.
This paper examines the convergence of sentiment analysis with distributed systems, comparing performance between single-node and distributed architectures through experiments that show distributed systems can process data 3.2 times faster while maintaining 98.5% accuracy.
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.