SentimentViz: Leveraging RoBERTa in Python for Advanced Sentiment Analysis and Visualization in the FMCG Sector

In a data-driven world where governments and businesses seek insights from vast amounts of unstructured text data, sentiment analysis plays a pivotal role in decision-making. Sentiment analysis helps analyze cus-tomer experience, ultimately helping manage customer engagement. To build on this need for deeper sentiment understanding and scalable solutions, SentimentViz is an accelerator that leverages Python and chooses the best methodology for text-mining problems. It enables real-time analysis with robust visualization capabilities. In this study, the Sentiment Viz accelerator is leveraged to estimate the sentiment of 9 different Patanjali prod-ucts using a strong data science framework and best-of-the-class ML techniques. This accelerator can predict sentiment for various products and services, categorizing them as positive, negative, or neutral. Once deployed, it enables real-time sentiment mapping for any product or service.

在数据驱动的世界中，政府与企业寻求从海量非结构化文本数据中提炼洞见，情感分析在决策过程中发挥着核心作用。情感分析有助于洞察客户体验，进而助力客户参与度的管理。为满足对更深层次情感理解及可扩展解决方案的需求，SentimentViz 作为一项加速器，依托 Python 平台并选用最优文本挖掘方法。它具备强大的可视化能力，实现实时分析。在本研究中，Sentiment Viz 加速器被应用于借助强大的数据科学框架和一流的机器学习技术，对 9 种不同的 Patanjali 产品进行情感估算。该加速器能够预测各类产品及服务的情感倾向，将其归类为正面、负面或中性。一旦部署，它能够实现任何产品或服务的实时情感映射。