Marker Collective

Traditional sentiment analysis tools rely heavily on keyword matching, proximity scoring, or basic machine learning models. These approaches often fail to account for context, nuance, and complex narratives, leading to misleading results. For example, balanced articles that include both positive and negative elements may be misclassified, while industry-specific language, irony, or constructive criticism can be interpreted incorrectly. Some systems assign a single sentiment score to an entire article, which obscures how a specific company, product, executive, or message is actually portrayed. Delve addresses these limitations by using GenAI models that analyze the entire article, not just selected phrases, and apply sentiment at multiple levels. This includes overall article sentiment, subject-specific sentiment, competitor sentiment, topic sentiment, and key message sentiment. By treating sentiment as one structured signal among many—alongside relevance, themes, message pull-through, and context—teams avoid overreliance on a single score and provide additional analytic granularity.