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The SENTIMENT Lab’s flow involves a systematic process of data collection, preprocessing, NLP algorithms, sentiment classification, and presentation of results. This AI solution enables organizations to extract sentiment from a large volume of news articles and gain valuable insights into public sentiment on a global scale.



Data Collection

The process starts with the collection of data. Sentiment Lab processes over 250,000 news articles per day from more than 13,500 global sources. Once the data is collected, it undergoes preprocessing. This step involves cleaning and organizing the data to remove any noise or irrelevant information that might affect the sentiment analysis process.

AI-Powered Engine

After preprocessing, Sentiment Lab applies Artificial Intelligence (AI) algorithms to the data in order to assess financial text comprehension. Natural Language Processing (NLP) algorithms help analyze and understand the text by considering the semantic and syntactic meaning of the words and phrases used in the news articles. Sentiment classification involves determining whether the sentiment expressed in the article is positive, negative, or neutral.

Presentation

Once the sentiment of each news article is classified, Sentiment Lab presents the results. It could be in the form of visualizations, reports, or any other format that provides insights into the sentiment analysis. These results can be used by businesses, researchers, or individuals to gain valuable insights into public opinion and sentiment towards various topics.