Sentiment Analysis
What will I learn?
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. This sample will provide you with the knowledge and practical skills needed to analyze and extract meaningful insights from text data.
Agenda
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.
Who is this suitable for?
Aimed at complete beginners to KDB.AI. No prior experience required but python knowledge will make it easier to follow along.
To access your own KDB.AI instance as shown in the video simply click the blue button above or else signup here.
The jupyter notebook shown in the video is available to download here. Note, you will need to install the requirements.txt file in the repository to make sure yo have all the relevent Python packages needed to run the code.