CMSC 178DA | Week 11 · Session 1

Mining Meaning
from Text

From raw words to actionable insights

Department of Computer Science

University of the Philippines Cebu

"80% of enterprise data is unstructured — and much of it is text."

"The limits of my language mean the limits of my world."

— Ludwig Wittgenstein, 1921

Today: how to teach machines to understand text — and how to use that power responsibly.

Agenda

Session 1 Objectives

Text Preprocessing

Tokenize, clean, and normalize raw text into analysis-ready tokens.

Vectorization

Convert words into numbers using Bag of Words and TF-IDF representations.

Sentiment & Topics

Classify polarity with sentiment analysis and discover themes with LDA topic modeling.

Part I

Turning Noise
Into Signal

Natural language is messy. Preprocessing cleans, normalizes, and tokenizes text before any model can learn.

Part I — Text Preprocessing

Why Text Analytics?

Unstructured text data is everywhere — and growing faster than any other data type.

Text Data Sources

Customer reviews & feedback
Social media posts & comments
Support tickets & emails
News articles & reports
Survey open-ended responses

Bar chart showing 80% of enterprise data is unstructured text

Part I — Text Preprocessing

The Preprocessing Pipeline

Flowchart: Raw Text to Lowercase to Tokenize to Remove Stopwords to Lemmatize

Part I — Text Preprocessing

Preprocessing in Python

NLTK Library

The Natural Language Toolkit provides tokenizers, stemmers, lemmatizers, and stopword lists for 20+ languages.

Key Functions

word_tokenize() — split into words
stopwords.words() — common words list
WordNetLemmatizer() — dictionary lookup

Caveat: POS Tagging

WordNet defaults to noun POS — “running” stays as-is. Pass pos='v' for verb lemmatization to get “run”.

import nltk
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer

def preprocess(text):
    text = text.lower()
    tokens = word_tokenize(text)
    stop_words = set(stopwords.words('english'))
    tokens = [t for t in tokens
              if t not in stop_words and t.isalpha()]
    lemmatizer = WordNetLemmatizer()
    tokens = [lemmatizer.lemmatize(t) for t in tokens]
    return tokens

# Example
preprocess("The dogs are running FAST!")
# Output: ['dog', 'running', 'fast']

Part I — Text Preprocessing

Stemming vs. Lemmatization

Comparison of stemmer vs lemmatizer outputs for various words

Part II

Making Words
Countable

Machines need numbers, not words. Bag of Words and TF-IDF convert text into vector space.

Part II — Text Representation

Bag of Words

The simplest text representation: count how many times each word appears.

Limitations

Loses word order entirely
Common words dominate the counts
High-dimensional, sparse matrices

from sklearn.feature_extraction.text import CountVectorizer

corpus = [
    "The food was good",
    "The service was bad",
    "Good food but bad service"
]

vectorizer = CountVectorizer()
X = vectorizer.fit_transform(corpus)

print(vectorizer.get_feature_names_out())
# ['bad', 'but', 'food', 'good', 'service', 'the', 'was']

print(X.toarray())
# [[0, 0, 1, 1, 0, 1, 1],
#  [1, 0, 0, 0, 1, 1, 1],
#  [1, 1, 1, 1, 1, 0, 0]]

Part II — Text Representation

TF-IDF: Important Words Get Higher Weight

Three-panel chart showing TF, IDF, and TF-IDF scores for the, data, and regression

Part II — Text Representation

TF-IDF in Python

Key Parameters

max_features — limit vocabulary size
min_df — ignore very rare terms
max_df — ignore very common terms
ngram_range — include bigrams

TL;DR

TF-IDF = TF × log(N/df). Words that are frequent in a document but rare across the corpus get the highest score.

from sklearn.feature_extraction.text import TfidfVectorizer

tfidf = TfidfVectorizer(
    max_features=1000,
    min_df=5,
    max_df=0.95,
    ngram_range=(1, 2)  # Unigrams + bigrams
)

X_tfidf = tfidf.fit_transform(documents)

# Most important terms per document
feature_names = tfidf.get_feature_names_out()
for i, doc in enumerate(X_tfidf):
    top_indices = doc.toarray().argsort()[0][-5:]
    top_terms = [feature_names[j] for j in top_indices]
    print(f"Doc {i}: {top_terms}")

Part II — Text Representation

TF-IDF Scores Across Documents

Heatmap of TF-IDF scores showing common words score low and distinctive words score high

Knowledge Check

Which term has the HIGHEST TF-IDF score?

A) “the” — appears in every document

B) “analytics” — appears in 2 of 100 docs, 5× each

C) “data” — appears in 80 of 100 docs

D) “I” — common stopword

Click & hold to reveal answer

✓ Correct: B) “analytics”

High TF (appears 5 times in those docs) × high IDF (only 2/100 docs) = highest TF-IDF. Terms A, C, D have low IDF because they appear in most documents.

Part III

Reading Between
the Lines

Sentiment analysis classifies text polarity — positive, negative, or neutral — using lexicons or machine learning.

Part III — Sentiment Analysis

Three Approaches to Sentiment Analysis

Comparison of lexicon-based, ML-based, and pre-trained sentiment approaches

Part III — Sentiment Analysis

VADER Sentiment

What is VADER?

Valence Aware Dictionary and sEntiment Reasoner. Rule-based, tuned for social media text.

Compound Score

Ranges from -1 (most negative) to +1 (most positive). Threshold: >0.05 positive, <-0.05 negative.

from nltk.sentiment import SentimentIntensityAnalyzer

nltk.download('vader_lexicon')
sia = SentimentIntensityAnalyzer()

texts = [
    "This product is amazing! I love it!",
    "Terrible experience, never buying again",
    "It's okay, nothing special"
]

for text in texts:
    scores = sia.polarity_scores(text)
    print(f"{text}")
    print(f"  Compound: {scores['compound']:.2f}")

# Output:
# "This product is amazing..." → Compound: 0.86
# "Terrible experience..."     → Compound: -0.48
# "It's okay, nothing..."      → Compound: -0.09

Part III — Sentiment Analysis

TextBlob Sentiment

Two Dimensions

Polarity: -1 (negative) to +1 (positive)
Subjectivity: 0 (factual) to 1 (opinionated)

Per-Sentence Analysis

Analyze each sentence separately for mixed-sentiment texts like product reviews.

from textblob import TextBlob

# Whole-sentence analysis
text = "The food was delicious but the service was slow"
blob = TextBlob(text)
print(f"Polarity:     {blob.sentiment.polarity:.2f}")
print(f"Subjectivity: {blob.sentiment.subjectivity:.2f}")
# Output: Polarity: 0.35, Subjectivity: 0.80

# Per-clause analysis (split manually)
for clause in ["The food was delicious",
               "The service was slow"]:
    pol = TextBlob(clause).sentiment.polarity
    print(f"'{clause}' → {pol:.2f}")

# Output:
# "The food was delicious" → 1.00
# "The service was slow"   → -0.30

Part III — Philippine Context

Philippine Social Media Sentiment

Philippine Social Media

With 98M internet users and 95M social media accounts (DataReportal 2026), the Philippines is one of the most active countries online. Sentiment analysis helps brands, government, and researchers understand public opinion.

Caveat

VADER was designed for English text. Filipino/Taglish posts may need custom lexicons or translated models for accurate results.

Part III — Sentiment Analysis

ML Sentiment Pipeline

TF-IDF + Naive Bayes

A simple yet effective pipeline: vectorize text with TF-IDF, then classify with Multinomial Naive Bayes.

When to Use ML-Based

Domain-specific language (medical, legal)
You have labeled training data
Lexicon approaches underperform

from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import Pipeline
from sklearn.model_selection import train_test_split

# Labeled training data
X_train = ["great product", "terrible quality", ...]
y_train = [1, 0, ...]  # 1=positive, 0=negative

# Pipeline: TF-IDF + Naive Bayes
pipeline = Pipeline([
    ('tfidf', TfidfVectorizer()),
    ('clf', MultinomialNB())
])

pipeline.fit(X_train, y_train)

# Predict new text
predictions = pipeline.predict(["I love this!"])
# Output: [1]  (positive)

Part IV

Discovering
Hidden Themes

LDA topic modeling reveals latent topics. NER extracts named entities. Word clouds visualize term frequencies.

Part IV — Topic Modeling

LDA Topic Modeling

Latent Dirichlet Allocation

Unsupervised algorithm that discovers hidden topics in a collection of documents.

Key Assumptions

Documents are mixtures of topics
Topics are distributions over words
You choose number of topics (k)
Input must be word counts, not TF-IDF

from sklearn.decomposition import LatentDirichletAllocation
from sklearn.feature_extraction.text import CountVectorizer

# LDA needs word COUNTS (not TF-IDF!)
cv = CountVectorizer(max_features=5000,
                     stop_words='english')
X_counts = cv.fit_transform(documents)

lda = LatentDirichletAllocation(
    n_components=5, random_state=42)
lda.fit(X_counts)

# Print top words per topic
names = cv.get_feature_names_out()
for i, topic in enumerate(lda.components_):
    top = [names[j] for j in topic.argsort()[-10:]]
    print(f"Topic {i}: {', '.join(top)}")

# Topic 0: price, quality, value, product...
# Topic 1: delivery, shipping, days, arrive...

Part IV — Philippine Context

Handling Filipino Text

Before and after Filipino text preprocessing showing stopword removal

Session 1: Key Takeaways

Preprocessing is critical — lowercase, tokenize, remove stopwords, lemmatize
TF-IDF weights important terms higher than common words
Sentiment analysis has three approaches: lexicon, ML, and pre-trained
LDA discovers hidden topics in document collections
Filipino text needs custom stopwords and Taglish handling

Next: Analytics at Scale & Ethics

Big data tools, privacy regulations, algorithmic bias, and responsible AI.

CMSC 178DA | Week 11 · Session 2

Scale, Privacy
& Fairness

When data gets big, ethics must get bigger

Department of Computer Science

University of the Philippines Cebu

"With great data comes great responsibility."

The Philippine Data Explosion

86M+

GCash registered users

98M

Internet users in PH

95M

Social media accounts

Who protects this data?

Agenda

Session 2 Objectives

Big Data Tools

When pandas isn’t enough: Spark, cloud platforms, and the 5 Vs of big data.

Privacy & Compliance

GDPR, Philippine DPA (RA 10173), anonymization, and consent requirements.

Bias & Fairness

Sources of algorithmic bias, fairness metrics, mitigation strategies, and responsible AI.

Part I

When pandas
Is Not Enough

Big data demands distributed computing. Learn when and why to scale beyond a single machine.

Part I — Analytics at Scale

The 5 Vs of Big Data

Pentagon diagram showing Volume, Velocity, Variety, Veracity, Value

Part I — Analytics at Scale

When Do You Need Big Data Tools?

× You DON’T need Spark if:

Data fits in memory (<16 GB)
Processing is one-time, ad-hoc
Simple aggregations / filters
pandas + SQL handles it fine

✓ You DO need distributed tools when:

Data exceeds single machine memory
Processing must be parallelized
Real-time streaming is required
ML at scale (millions of records)

TL;DR

Most analytics tasks (<10 GB) don’t need Spark. Use the simplest tool that works.

Part I — Analytics at Scale

Apache Spark

Distributed Computing

In-memory processing (up to 100× faster than MapReduce in memory; typically 3–10× in practice)
Supports Python (PySpark), SQL, Scala, R
MLlib for machine learning at scale

When to Choose Spark

Datasets >100 GB, iterative ML algorithms, streaming data, or when a single machine can’t keep up.

from pyspark.sql import SparkSession

spark = SparkSession.builder \
    .appName("Analytics") \
    .getOrCreate()

# Load CSV (distributed across cluster)
df = spark.read.csv("large_data.csv",
                    header=True,
                    inferSchema=True)

# SQL-like operations at scale
df.groupBy("region") \
  .agg({"sales": "sum"}) \
  .orderBy("sum(sales)", ascending=False) \
  .show()

# Result: distributed across worker nodes

Part I — Analytics at Scale

Cloud Analytics Platforms

Platform	Service	Strength	Pricing Model
AWS	Redshift, Athena, EMR	Most comprehensive ecosystem	Per-query or provisioned
GCP	BigQuery	Serverless SQL at scale	Per-TB scanned
Azure	Synapse Analytics	Enterprise integration (Office 365)	DWU-based

-- BigQuery example: analyze sales by region (serverless, no cluster setup)
SELECT region, SUM(sales) as total_sales, COUNT(*) as num_transactions
FROM `project.dataset.sales_table`
WHERE date >= '2024-01-01'
GROUP BY region
ORDER BY total_sales DESC

Knowledge Check

Your dataset is 500 MB of CSV files.
Which tool should you use?

A) Apache Spark cluster

B) pandas on your laptop

C) Google BigQuery

D) Hadoop MapReduce

Click & hold to reveal answer

✓ Correct: B) pandas on your laptop

500 MB fits comfortably in memory. No need for distributed computing overhead. Use the simplest tool that works!

Part II

The Right to
Be Forgotten

Privacy regulations like GDPR and the Philippine DPA define how data can be collected, used, and stored.

Part II — Data Privacy

Data Privacy Regulation Timeline

Timeline of privacy regulations from 1995 EU Directive to 2020 CCPA

GDPR (EU, 2018)

Gold standard for privacy. Right to deletion, data portability, fines up to 4% of global revenue.

Philippine DPA (2012)

RA 10173. Enforced by NPC. Consent, purpose limitation, breach notification within 72 hours.

CCPA (California, enacted 2018, effective 2020)

Consumer right to know, delete, and opt-out of data sales. Applies to large businesses.

Part II — Philippine Context

Philippine Data Privacy Act (RA 10173)

Consent

Freely given, specific, and informed

Purpose

Use only for stated purpose

Minimization

Collect only what’s needed

Accuracy

Keep data up to date

Retention

Delete when no longer needed

Part II — Data Privacy

Anonymization Techniques

Technique	Description	Example
Masking	Hide partial data	“Juan D.”
Generalization	Broaden categories	Age 25 → “20–30”
Suppression	Remove identifiers	Remove SSN column
Noise Addition	Add random values	Salary ± 5%
K-Anonymity	Ensure k similar records	5+ with same quasi-identifiers

df['name'] = df['name'].str[0] + '***'           # Masking
df['age_group'] = pd.cut(df['age'],               # Generalization
    bins=[0, 20, 30, 40, 50, 100],
    labels=['<20', '20-30', '30-40', '40-50', '50+'])

Part II — Data Privacy

K-Anonymity

Definition

A dataset satisfies k-anonymity if every combination of quasi-identifiers (age, ZIP, gender) matches at least k other records.

Why k ≥ 5?

With k=5, an attacker can narrow a person down to at most 1-in-5 records — not enough to re-identify.

Before and after k-anonymity showing generalized data

Part III

When Algorithms
Discriminate

Bias in data becomes bias in decisions. Understanding sources and metrics is the first step toward fairness.

Part III — Algorithmic Bias

Four Sources of Algorithmic Bias

Four-quadrant diagram of bias sources: Historical, Representation, Measurement, Aggregation

Part III — Case Studies

Real-World Bias Failures

COMPAS (Criminal Justice)

Predicted recidivism risk for sentencing. ProPublica found it produced higher false positive rates for Black defendants than white defendants. Used in real sentencing decisions.

Amazon Hiring Tool (HR)

Trained on 10 years of (mostly male) hiring data. The model learned to penalize resumes containing the word “women’s”. Amazon scrapped the entire program.

Part III — Algorithmic Bias

Detecting Bias

Check Metrics by Group

Split predictions by demographic and compare error rates. Significant differences indicate bias.

What to Look For

Unequal false positive rates (FPR)
Unequal false negative rates (FNR)
Different accuracy across groups

from sklearn.metrics import confusion_matrix

# Check metrics by demographic group
for group in df['demographic'].unique():
    subset = df[df['demographic'] == group]
    cm = confusion_matrix(
        subset['actual'],
        subset['predicted']
    )

    # False positive rate
    fpr = cm[0, 1] / (cm[0, 0] + cm[0, 1])
    # False negative rate
    fnr = cm[1, 0] / (cm[1, 0] + cm[1, 1])

    print(f"{group}: FPR={fpr:.3f}, FNR={fnr:.3f}")

# If FPR differs significantly across groups
# → your model has disparate impact

Part III — Algorithmic Bias

Fairness Metrics

Metric	Definition	When to Use	Example
Demographic Parity	Equal positive prediction rates across groups	Hiring, lending	Same loan approval rate for all demographics
Equalized Odds	Equal TPR and FPR across groups	Criminal justice	Same error rates regardless of race
Calibration	Equal precision across groups	Medical diagnosis	70% confidence means 70% correct for all groups

Part III — Algorithmic Bias

Disparate Error Rates Across Groups

Grouped bar chart showing FPR and FNR across demographic groups

Part III — Algorithmic Bias

Mitigating Bias: Three Stages

Pre-processing

Fix the data before training. Rebalance datasets, remove proxy features, use synthetic oversampling (SMOTE).

In-processing

Fix the algorithm. Add fairness constraints to the loss function, use adversarial debiasing, or fair representation learning.

Post-processing

Fix the output. Adjust decision thresholds per group, calibrate probabilities, or audit and correct predictions.

Part IV

Building AI That
Serves Everyone

Explainability, accountability, and ethics must be designed in — not bolted on after deployment.

Part IV — Responsible AI

Explainability: SHAP & LIME

Why Explainability?

GDPR right to “meaningful information about the logic involved” (Art. 13–15)
Build trust with stakeholders
Debug model errors and biases
Regulatory compliance

Key Tools

SHAP: Shapley values — fair attribution of each feature’s contribution
LIME: Local explanations via interpretable surrogate models

SHAP waterfall plot showing feature contributions to prediction

Part IV — Responsible AI

The FATE(S) Framework

Fairness

Equal treatment across demographic groups

Accountability

Clear ownership and responsibility for outcomes

Transparency

Explainable decisions and open processes

Ethics

Consider societal impact and human values

Safety

Prevent harm to users and communities

Part IV — Philippine Context

Ethics Challenges in the Philippines

GCash Credit Scoring

How do you score creditworthiness for informal economy workers with no traditional credit history? What biases might emerge?

DOH Disease Prediction

Rural areas have less data, worse connectivity. Models trained on urban data may fail in provinces where they’re needed most.

Facial Recognition

Commercial facial recognition has higher error rates for darker skin tones and women. Deployed in Philippine malls and airports.

Social Media Monitoring

With 95M accounts, social media surveillance raises privacy concerns. Where is the line between public safety and privacy?

Session 2: Key Takeaways

Big data tools are needed only when scale demands it — don’t over-engineer
Philippine DPA (RA 10173) governs data privacy; know its five principles
Algorithmic bias enters through data, features, and modeling choices
Fairness metrics help quantify bias — but you can’t satisfy all of them
Responsible AI (FATE) requires continuous attention, not one-time audits

Lab 11: Bias Audit Project

Audit a model for bias, calculate fairness metrics, and propose mitigation strategies.

Mining Meaningfrom Text

"The limits of my language mean the limits of my world."

Session 1 Objectives

Text Preprocessing

Vectorization

Sentiment & Topics

Turning NoiseInto Signal

Why Text Analytics?

Text Data Sources

The Preprocessing Pipeline

Why Every Step Matters

Order Matters Too

Preprocessing in Python

NLTK Library

Key Functions

Caveat: POS Tagging

Stemming vs. Lemmatization

Making WordsCountable

Bag of Words

Limitations

TF-IDF: Important Words Get Higher Weight

TF (Term Frequency)

IDF (Inverse Document Frequency)

TF-IDF in Python

Key Parameters

TF-IDF Scores Across Documents

Low Scores (Blue)

High Scores (Dark Blue)

Which term has the HIGHEST TF-IDF score?

✓ Correct: B) “analytics”

Reading Betweenthe Lines

Three Approaches to Sentiment Analysis

Lexicon-Based

ML-Based

Pre-trained

VADER Sentiment

What is VADER?

Compound Score

TextBlob Sentiment

Two Dimensions

Per-Sentence Analysis

Philippine Social Media Sentiment

Philippine Social Media

Caveat

ML Sentiment Pipeline

TF-IDF + Naive Bayes

When to Use ML-Based

DiscoveringHidden Themes

LDA Topic Modeling

Latent Dirichlet Allocation

Key Assumptions

Handling Filipino Text

Filipino Stopwords

Code-Switching Challenge

Session 1: Key Takeaways

Next: Analytics at Scale & Ethics

Scale, Privacy& Fairness

The Philippine Data Explosion

Session 2 Objectives

Big Data Tools

Privacy & Compliance

Bias & Fairness

When pandasIs Not Enough

The 5 Vs of Big Data

Beyond Just Size

Philippine Example

When Do You Need Big Data Tools?

Apache Spark

Distributed Computing

When to Choose Spark

Cloud Analytics Platforms

Your dataset is 500 MB of CSV files.Which tool should you use?

✓ Correct: B) pandas on your laptop

The Right toBe Forgotten

Data Privacy Regulation Timeline

GDPR (EU, 2018)

Philippine DPA (2012)

CCPA (California, enacted 2018, effective 2020)

Philippine Data Privacy Act (RA 10173)

Consent

Mining Meaning
from Text

Turning Noise
Into Signal

Making Words
Countable

Reading Between
the Lines

Discovering
Hidden Themes

Scale, Privacy
& Fairness

When pandas
Is Not Enough

Your dataset is 500 MB of CSV files.
Which tool should you use?

The Right to
Be Forgotten

When Algorithms
Discriminate

Building AI That
Serves Everyone