Sentiment Analysis Pipeline

Sentiment Analysis Pipeline

Sentiment analysis decides whether a piece of text expresses a positive, negative, or neutral feeling. A working system is a pipeline of stages that transform raw text into a label.

Stages of the pipeline

• Cleaning removes noise like markup, fixes casing, and handles punctuation.
• Tokenization splits text into words or subwords.
• Vectorization turns tokens into numbers using bag of words, TF IDF, or embeddings.
• Classification applies a trained model to output a sentiment label.

Each stage feeds the next, and a change early in the pipeline can ripple downstream. Keeping the same steps for training and prediction is essential, or the model sees different inputs than it learned from.

A common pitfall

Negation flips meaning, so not good is negative even though good is positive. Simple word count models miss this unless features capture short phrases. This is why context aware embeddings often outperform plain bag of words for sentiment.

Key idea

A sentiment pipeline cleans, tokenizes, vectorizes, and classifies text, and it must handle negation and apply identical steps at training and prediction time.