94% accuracy in Sentiment Analysis

Language-Agnostic Performance

Our sentiment analysis methodology is completely language-agnostic, allowing us to process and analyze content in any language with consistent accuracy. Whether you’re working with English, Greek, Spanish, Mandarin, or any other language, our models maintain their high performance standards. This cross-lingual capability ensures that global brands and multilingual content creators can rely on our services regardless of their target markets or content languages.

Scalable Content Processing

Our methodology seamlessly adapts to content of any length and complexity. From brief X (Twitter) posts of just a few words to extensive, structured blog posts and articles, our system maintains consistent performance. The models automatically adjust their processing approach based on the text characteristics, ensuring that short-form social media content receives the same level of accurate sentiment classification as long-form editorial content.

Traditional Ensemble Approach – 92% Accuracy

At Palowise, we have discovered that using multiple models for sentiment analysis can be advantageous in several ways. Firstly, it allows us to have more control over the types of errors that the model could make. For instance, as we provide a negative alert service, we mustn’t miss any negative mentions. Even though this method does not always guarantee higher accuracy, it can assist us in capturing the most negative mentions by utilizing the predictions from multiple models together. Secondly, combining models can help us identify problematic mentions that cause significant conflict between different prediction models. We can then assign these mentions to our analysts for further examination and classification. Lastly, by using multiple models, we can reduce the reliance of individual models on the amount of training data we have available. As our analysts classify a certain percentage of mentions for each client every month, the volume of training data for each project increases gradually. Individual models can perform differently depending on the amount of training data, so combining models can help us overcome this issue to achieve better performance as we have more data to work with.

Breakthrough LLM Approach – 94% Accuracy

For specific projects requiring the highest level of accuracy and customization, we offer our revolutionary LLM-powered sentiment analysis approach. This cutting-edge methodology incorporates all peculiarities, specificities, and custom needs per client, delivering truly personalized sentiment classification that understands your unique business context and industry nuances.

Our breakthrough approach utilizes multiple state-of-the-art large language models working in concert with various fine-tuned prompts specifically designed for your project requirements. These diverse LLM insights are then fed to a final LLM annotator that makes the ultimate classification decision, combining the strengths of multiple AI systems to achieve superior accuracy.

The revolutionary advantage of this approach is its ability to deliver high-performance results without requiring annotated training data. Unlike traditional machine learning methods that need extensive labeled datasets, our LLM approach can be deployed and begin delivering accurate predictions within just a few days of project initiation. This rapid deployment capability makes it ideal for time-sensitive projects or clients who need immediate sentiment insights without the typical data preparation overhead.

Flexible Implementation

Whether we use our traditional ensemble approach for consistent, reliable results or our advanced LLM methodology for maximum accuracy and customization, both solutions maintain our commitment to cross-lingual performance and scalable content processing. Our team works closely with each client to determine the optimal approach based on their specific accuracy requirements, timeline constraints, and customization needs.