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Sentiment_Data_Analysis_Using_Multinomial_Naive_Bayes

Overview

Techniques Used

  • Bag-of-Words (BoW) feature representation
  • Multinomial Naive Bayes
  • Laplace smoothing (tested across different smoothing factors)

Experiments Included

  1. Processed Text + Naive Bayes (smoothing factor comparison)
  2. Raw Text + Naive Bayes (smoothing factor comparison)
  3. Removing Rare Words (frequency threshold < 5 removed)
  4. Impact of Sentiment on Review Lengths (distribution analysis)

Experiments & Key Results

Experiment 1 — Processed Text (BoW + Naive Bayes)

Preprocessing applied: tokenization + stopword removal.
Smoothing factors evaluated: 0.1, 0.3, 0.5, 1.0, 1.5, 1.8, 1.9, 2.0.

Best result: smoothing factor 1.8

  • Precision 0.85592
  • Recall 0.84009
  • F1 0.84793

📌 Figure: Impact of smoothing factors on precision/recall/F1

Figure 1

Experiment 2 — Raw Text (BoW + Naive Bayes)

No preprocessing (raw reviews).
Smoothing factors evaluated: 0.1 → 3.0.

Best result: smoothing factor 0.5

  • Precision 0.88033
  • Recall 0.84914
  • F1 0.86445

📌 Figure: Smoothing factors vs precision/recall/F1 using raw text

Figure 2

Experiment 3 — Removing Rare Words

Rare words appearing < 5 times are removed to reduce vocabulary size and noise.

  • Rare words identified: 70,178
  • Metrics after retraining:
    • Precision 0.8534
    • Recall 0.8425
    • F1 0.8479

Comparison (Cleaned Rare Words vs Processed Text):

Metric Cleaned Rare Words Processed Text
Precision 0.8534 0.8553
Recall 0.8425 0.8405
F1 Score 0.8479 0.8478

📌 Figure: Cleaned rare words vs processed text

Figure 3

Experiment 4 — Impact of Sentiment on Review Lengths

This experiment analyzes the relationship between review length and sentiment label.

Observation:

  • Negative reviews are more concentrated around shorter lengths.
  • Positive reviews show a slightly broader spread.
  • There is no clear length threshold that cleanly separates positive vs negative, but length may still help as a feature in ambiguous cases.

📌 Figure: Histogram of review lengths by sentiment

Figure 4

Summary of Findings (Experiments 1–4)

  • Raw text Naive Bayes performed better than processed text in terms of F1 score, suggesting raw text retains helpful context.
  • Removing rare words provides similar overall performance while reducing complexity and potentially improving efficiency.
  • Review length alone is not a reliable sentiment indicator, but trends suggest it could be a useful auxiliary feature.