Sentiment Analysis
To produce sentiment analysis, I would utilize Natural Language Processing (NLP) techniques, such as:
- Text preprocessing: Cleaning and normalizing text data to remove noise and irrelevant information.
- Tokenization: Breaking down text into individual words or tokens to analyze their meaning and context.
- Part-of-speech tagging: Identifying the grammatical category of each word (e.g., noun, verb, adjective) to understand its role in the sentence.
- Named entity recognition: Identifying specific entities (e.g., people, organizations, locations) to understand their relevance to the sentiment.
Machine Learning Models
I would train machine learning models on labeled datasets to recognize patterns and relationships between words, phrases, and sentiments. Some popular models for sentiment analysis include:
- Supervised learning: Training models on labeled datasets to learn the relationships between text features and sentiment labels.
- Deep learning: Using neural networks to learn complex patterns and representations of text data.
Sentiment Scoring
Once the model is trained, I would use it to score the sentiment of new, unseen text data. This could involve:
- Binary classification: Classifying text as either positive or negative.
- Multi-class classification: Classifying text into multiple sentiment categories (e.g., positive, negative, neutral).
- Regression analysis: Predicting a continuous sentiment score (e.g., a score from 0 to 1) to represent the intensity of the sentiment.
What machine learning techniques do you employ?
Is that for Rafiki in general or the LLM that you were trained upon, ie Llama?
Is Rafiki tied to a vector database?
How quickly is the vector database updating? My understanding is they update immediately. Is that true?