Social listening classification

We partnered with Semantiweb, a pioneer in social listening analytics, to overcome their challenge of refining their machine learning model for enhanced multilingual capabilities. Semantiweb's existing model struggled with accurately processing and analyzing social media data in multiple European languages, limiting their ability to provide clients with precise and culturally relevant insights.


Semantiweb, operating at the forefront of social listening analytics, faced a complex challenge in their quest to refine their machine learning model. Their existing model, while functional, was not fully optimized to handle the nuances and complexities of multiple European languages. As a result, Semantiweb struggled to provide their diverse clientele with the level of accuracy and relevance in insights they aspired to deliver.

Recognizing the critical importance of multilingual capabilities in today's interconnected digital landscape, Semantiweb sought to enhance their machine learning model. Their goal was to ensure it could effectively process and analyze social media data in five key European languages. This enhancement was not just about expanding linguistic reach; it was crucial for delivering deeper, more culturally and contextually relevant insights to their clients. Semantiweb aimed to harness the power of advanced machine learning to capture the true essence of social conversations across Europe, thus offering their clients a more nuanced and comprehensive understanding of public sentiment.


In response to Semantiweb's challenge, we embarked on a strategic collaboration aimed at elevating their machine learning capabilities. Our approach centered on the integration of advanced natural language processing (NLP) techniques, which are critical in understanding and interpreting the complexities of language in a nuanced and contextually accurate manner.

Recognizing that the effectiveness of a machine learning model is intrinsically linked to the quality of its training data, we focused on significantly enhancing the diversity and quality of the data sets for each of the five European languages. This involved curating a rich and varied range of linguistic inputs, encompassing different dialects, idiomatic expressions, and cultural references, to ensure a comprehensive understanding of each language's unique attributes.

By infusing Semantiweb's machine learning model with this enhanced data and sophisticated NLP techniques, we aimed to provide a more robust, accurate, and culturally attuned analysis of social media sentiments. This solution was designed not only to meet the immediate needs of Semantiweb but also to establish a scalable and adaptable framework for future expansion into additional languages and markets.


The culmination of our collaborative efforts with Semantiweb led to a significant breakthrough in their social listening capabilities. The enhanced machine learning model, powered by advanced NLP techniques and enriched with diverse, high-quality training data, demonstrated a remarkable improvement in both accuracy and relevance of social insights across the five European languages.

This advancement translated into tangible benefits for Semantiweb's clients. They were now equipped with more precise and culturally attuned insights, enabling them to understand the nuances of public sentiment more deeply. This newfound precision in social listening empowered them to make more informed decisions, tailor their marketing strategies more effectively, and engage with diverse European audiences in a way that resonated more authentically.

The success of this project not only fortified Semantiweb's position as a leader in social listening analytics but also set a new benchmark for multilingual sentiment analysis in the industry. Clients operating in multiple European markets could now leverage these enhanced insights to drive more impactful marketing campaigns and foster stronger connections with their target audiences.