Sentiment analysis, a branch of natural language processing (NLP) also known as opinion mining, is a powerful technique used to determine the sentiment expressed in text, whether it’s positive, negative, or neutral. In a world dominated by an overwhelming amount of textual data, understanding the emotions and opinions expressed in text has become increasingly vital. This sample will provide you with the knowledge and practical skills needed to analyze and extract meaningful insights from text data.
We will begin by loading in textual data and using a pretrained model to perform sentiment analysis to classify the data. Then we will create vector embeddings for the text and its attached metadata and insert these into our vector database, KDB.AI. We will then query the database to return general sentiments around different topics mentioned within the text and visualize our results.
The aim of this sample exercise is to show that we can combine KDB.AI’s similarity search with sentiment analysis to offer a powerful solution for a wide range of real-world problems. Whether you are a business owner aiming to gauge customer satisfaction, a marketer seeking to understand consumer opinions, or simply curious about the sentiment behind social media posts and reviews, sentiment analysis can provide valuable insights. With the ability to automatically classify text as positive, negative, or neutral, sentiment analysis enables us to tap into the collective emotional pulse of the online world.
Download the Jupyter Notebook and any accompanying files at the repository on GitHub.