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How to use modern language models for enhanced sentiment analysis

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          🞛 This publication is a summary or evaluation of another publication 🞛 This publication contains editorial commentary or bias from the source
Beyond simplistic sentiment charts, AI now dives into context, purpose and emotion to reveal what really drives customer opinions. The post How to use modern language models for enhanced sentiment analysis appeared first on MarTech.
The article from MSN discusses the application of modern language models in enhancing sentiment analysis, a technique used to determine the emotional tone behind a series of words or phrases. It explains how traditional methods like rule-based systems and basic machine learning models have limitations in understanding context, sarcasm, and nuanced expressions. Modern language models, such as those based on transformer architectures like BERT, RoBERTa, and XLNet, offer significant improvements by understanding context better through pre-training on vast datasets. These models can capture subtleties in language, including idioms, negations, and mixed sentiments, leading to more accurate sentiment classification. The article also touches on how these models can be fine-tuned for specific domains, improving their performance in industry-specific applications like customer feedback analysis, brand monitoring, and market research. Additionally, it highlights the integration of these models with other AI technologies to provide comprehensive insights, although it notes the challenges such as computational costs and the need for large, high-quality datasets for training.

Read the Full MSN Article at:
[ https://www.msn.com/en-us/technology/data-science/how-to-use-modern-language-models-for-enhanced-sentiment-analysis/ar-AA1xO4wg ]