Exp 11 — NLP analysis of restaurant reviews

3) Dataset

• Source: Use restaurant reviews dataset (Kaggle or course-provided) and cite it.
• Features/Labels: [Describe X and y; mention classes if classification]
• Split: [Train/Validation/Test or K-fold]
• Preprocessing: [Scaling/Normalization, resizing, tokenization, etc.]

4) Procedure / Steps

1. Load dataset and perform preprocessing.
2. Define model architecture and justify key choices.
3. Compile model (loss + optimizer + metrics).
4. Train with validation and log curves.
5. Evaluate on test set and compute required metrics.
6. Summarize observations and limitations.

5) Code (Skeleton)

Paste your complete runnable code below (or attach notebook link in the final submission).

import re
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, confusion_matrix

# TODO: load restaurant reviews dataset
# df = pd.read_csv(...)
# X = df['review_text']
# y = df['label']

vec = TfidfVectorizer(max_features=5000, ngram_range=(1,2))
Xv = vec.fit_transform(X)
X_train, X_test, y_train, y_test = train_test_split(Xv, y, test_size=0.2, random_state=42, stratify=y)

clf = LogisticRegression(max_iter=200)
clf.fit(X_train, y_train)
pred = clf.predict(X_test)
print(classification_report(y_test, pred))

6) Results / Output

• Metrics: [Write your final values: accuracy/F1 or MAE/MSE]
• Plots: [Attach loss/metric curves; prediction vs actual plots if forecasting]
• Screenshots: [Paste screenshots of outputs, confusion matrix, sample predictions]

7) Observations / Discussion

• [Observation 1: what changed when you tuned epochs/batch size?]
• [Observation 2: evidence of overfitting/underfitting?]
• [Observation 3: what improved performance (augmentation, regularization, fine-tuning)?]

8) Conclusion

Write 3–6 lines summarizing what you implemented, key result, and what you learned.

9) Viva Questions

1. What is TF-IDF and why is it useful?
2. What are n-grams?
3. What is the difference between training and validation data?
4. Explain overfitting and two ways to reduce it.
5. Why do we normalize/scale inputs?
6. What does batch size and epoch mean?
7. How do you choose a loss function for a task?

10) Post-lab Assignment

• Compare TF-IDF with Bag-of-Words.
• Try a deep model (Embedding + LSTM) and compare.