Lexicon-based approaches (VADER) in NLP - Model Pipeline Trace

Model Pipeline - Lexicon-based approaches (VADER)

This pipeline uses VADER, a lexicon-based tool, to analyze the sentiment of text. It scores words based on a dictionary and combines them to predict if the text is positive, negative, or neutral.

Data Flow - 5 Stages

1Input Text

1 sentence (string)→Raw text input for sentiment analysis→1 sentence (string)

"I love sunny days but hate the rain."

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2Text Preprocessing

1 sentence (string)→Lowercase conversion and punctuation handling→1 sentence (string)

"i love sunny days but hate the rain."

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3Tokenization

1 sentence (string)→Split sentence into words/tokens→8 tokens

["i", "love", "sunny", "days", "but", "hate", "the", "rain"]

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4Lexicon Scoring

8 tokens→Assign sentiment scores from VADER lexicon to each token→8 sentiment scores

[0.0, 3.2, 1.5, 0.0, 0.0, -3.5, 0.0, -1.0]

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5Aggregation

8 sentiment scores→Combine scores with rules for negation, intensity, and punctuation→4 sentiment metrics

{"positive": 0.45, "negative": 0.35, "neutral": 0.20, "compound": 0.34}

Training Trace - Epoch by Epoch

N/A

Epoch	Loss ↓	Accuracy ↑	Observation
1	N/A	N/A	VADER is a rule-based model; no training epochs.

Prediction Trace - 5 Layers

Layer 1: Input Text

Layer 2: Text Preprocessing

Layer 3: Tokenization

Layer 4: Lexicon Scoring

Layer 5: Aggregation

Model Quiz - 3 Questions

What does VADER use to assign sentiment scores to words?

A A dictionary of words with sentiment values

B A neural network trained on text

C Random guessing

D User feedback during prediction

Key Insight

VADER uses a simple dictionary of words with sentiment scores and combines them with rules to quickly and effectively analyze sentiment without training. It works well on social media text by handling negations, intensifiers, and punctuation.