Sentiment Analysis

Sentiment analysis is the process of detecting positive or negative sentiment in text. It is often used by businesses to detect sentiment in social data, gauge brand reputation, and understand customers.

Sentiment analysis focuses on the polarity of a text (positive, negative, neutral) but it also goes beyond polarity to detect specific feelings and emotions (angry, happy, sad), urgency (urgent, not urgent) and even intentions (interested or not interested).

Bidirectional Representation for Transformers (BERT)

BERT is a powerful technique for natural language processing that can improve how well computers comprehend human language. The foundation of BERT is the idea of exploiting bidirectional context to acquire complex and insightful word and phrase representations. By simultaneously examining both sides of a word’s context, BERT can capture a word’s whole meaning in its context, in contrast to earlier models that only considered the left or right context of a word. This enables BERT to deal with ambiguous and complex linguistic phenomena including polysemy, co-reference, and long-distance relationships.

Preprocessing Model

Text inputs need to be transformed to numeric token IDs and arranged in several Tensors before being input to BERT. TensorFlow Hub provides a matching preprocessing model for each of the BERT models discussed above, which implements this transformation using TF ops from the TF.text library.

Model fine-tuning

Model fine-tuning is the process of taking a pre-trained machine learning model and further training it on a smaller, task-specific dataset. This helps the model adapt to a particular application or domain. Fine-tuning leverages the knowledge the model has gained during its initial training on a large dataset for a more general task. By using a fine-tuning dataset related to the specific task at hand, the model can improve its performance and accuracy for that targeted application. This process is crucial for customizing pre-trained models to perform effectively in specialized tasks or domains.