Streaming N-Gram Frequency Text Analyzer

What you will learn

• Normalize and tokenize raw unstructured text using regular expressions
• Construct multi-word n-gram sliding windows in Python
• Guard collections.Counter against IndexError when indexing most_common(n)
• Filter out domain-specific stop words to extract high-value keyphrases

What you need first

import re; s = 'Battery!'; print(re.sub(r'[^a-z0-9\s]', '', s.lower()))

battery

words = ['good', 'battery', 'life']; print([f'{words[i]} {words[i+1]}' for i in range(len(words)-1)])

['good battery', 'battery life']

from collections import Counter; c = Counter(); print(c.most_common(1)[0] if c else ('none', 0))

('none', 0)

Architecture and data flow

The data

Raw customer product review feedback strings containing punctuation, empty entries, and repeated keywords.

Step by step

Read each step, run its code, then check your output against the one shown.

1. 01

   Clean and tokenize raw customer review strings

   We begin by developing a text cleaning and tokenization function. Raw customer feedback frequently contains inconsistent uppercase letters, trailing punctuation, and extra whitespace. Our tokenizer converts input strings to lowercase and applies a regular expression to extract sequences of alphanumeric characters. We filter out empty tokens to produce a clean list of normalized word strings. We inspect the resulting token list to confirm that punctuation has been cleanly stripped before phrases are extracted by downstream analytics modules. This defensive parsing structure ensures that text processing pipelines process arbitrary document streams without throwing unhandled exceptions or corrupting frequency tallies.

   PythonStep 1Copy

   import re
   
   def tokenize(text: str) -> list[str]:
       cleaned = re.sub(r"[^a-zA-Z0-9\s]", " ", text.lower())
       return [w.strip() for w in cleaned.split() if w.strip()]
   
   sample_review = "The battery life is amazing! Great battery, but poor screen display..."
   tokens = tokenize(sample_review)
   print(f"Extracted {len(tokens)} tokens: {tokens}")

   Show outputGuess what it prints first

   Extracted 11 tokens: ['the', 'battery', 'life', 'is', 'amazing', 'great', 'battery', 'but', 'poor', 'screen', 'display']

   Common mistake

   Watch out: Using str.split(' ') without regular expression cleaning leaves punctuation attached to tokens, causing 'battery!' and 'battery' to be counted separately.

   How you will know

   The output confirms 11 clean tokens without trailing exclamation marks or ellipses.

2. 02

   Demonstrate IndexError from unguarded empty Counter indexing

   Wrong turn first: this step shows the mistake before the fix

   We demonstrate the exact technical flaw that crashed the production pipeline: indexing the result of Counter.most_common(1)[0] on empty inputs. Calling most_common(n) on an empty Counter does not raise an error; it returns an empty list []. However, attempting to index the first element [0] of that empty list immediately raises an IndexError. In batch pipelines, an empty customer review string causes this unguarded lookup to crash the entire data pipeline and abort processing mid-stream. This defensive parsing structure ensures that text processing pipelines process arbitrary document streams without throwing unhandled exceptions or corrupting frequency tallies.

   PythonStep 2Copy

   from collections import Counter
   
   empty_review = "..."
   tokens = [w for w in empty_review.split() if w.isalpha()]
   counts = Counter(tokens)
   
   print(f"most_common(1) on empty counter returns: {counts.most_common(1)}")
   
   try:
       # Naive unguarded indexing causes crash!
       top = counts.most_common(1)[0]
   except IndexError as e:
       print(f"Caught expected IndexError: {type(e).__name__} - index out of range!")

   Show outputGuess what it prints first

   most_common(1) on empty counter returns: []
   Caught expected IndexError: IndexError - index out of range!

   Common mistake

   Watch out: Claiming most_common() raises IndexError is technically wrong; most_common() returns an empty list, and the [0] index raises the IndexError.

   How you will know

   The output shows most_common(1) returns [] and indexing [0] raises an IndexError.

3. 03

   Construct n-gram sliding window generator

   Individual words often lack business context. For instance, the token 'battery' alone does not reveal whether the customer loved or hated the battery. Extracting bigrams (two-word phrases) like 'battery life' or 'poor battery' provides actionable sentiment insights. We construct an n-gram generator that iterates across the token list with a sliding window of size n. We inspect generated bigrams to verify sequential phrase extraction across consecutive words in the sentence without out-of-bounds slicing errors. This defensive parsing structure ensures that text processing pipelines process arbitrary document streams without throwing unhandled exceptions or corrupting frequency tallies.

   PythonStep 3Copy

   def get_ngrams(tokens: list[str], n: int = 2) -> list[str]:
       if len(tokens) < n:
           return []
       return [" ".join(tokens[i : i + n]) for i in range(len(tokens) - n + 1)]
   
   tokens = ["the", "battery", "life", "is", "amazing"]
   bigrams = get_ngrams(tokens, n=2)
   print(f"Generated {len(bigrams)} bigrams: {bigrams}")

   Show outputGuess what it prints first

   Generated 4 bigrams: ['the battery', 'battery life', 'life is', 'is amazing']

   Common mistake

   Watch out: Looping up to range(len(tokens)) without subtracting n + 1 causes slicing bounds to generate incomplete trailing n-grams.

   How you will know

   Output confirms 4 bigrams: ['the battery', 'battery life', 'life is', 'is amazing'].

4. 04

   Filter stop words and aggregate keyphrase frequencies

   Grammatical filler words such as 'the', 'is', 'and', 'but', and 'in' dominate raw frequency counts without carrying meaningful semantic value. We define a set of common stop words and filter them out during phrase generation. We use collections.Counter to aggregate keyphrase frequencies across the cleaned token stream. We inspect frequency counts to verify that informative domain phrases rise to the top of our analytical output rather than grammatical noise that skews executive sentiment summaries. This defensive parsing structure ensures that text processing pipelines process arbitrary document streams without throwing unhandled exceptions or corrupting frequency tallies.

   PythonStep 4Copy

   from collections import Counter
   import re
   
   STOP_WORDS = {"the", "is", "and", "but", "in", "to", "a", "of", "it"}
   
   def clean_tokens(text: str) -> list[str]:
       cleaned = re.sub(r"[^a-zA-Z0-9\s]", " ", text.lower())
       return [w for w in cleaned.split() if w not in STOP_WORDS]
   
   reviews = [
       "The battery life is amazing. Great battery!",
       "Poor battery life, but great screen display.",
       "Amazing screen display and great battery life."
   ]
   
   all_bigrams = []
   for r in reviews:
       tokens = clean_tokens(r)
       # Extract bigrams
       all_bigrams.extend([" ".join(tokens[i : i + 2]) for i in range(len(tokens) - 1)])
   
   counts = Counter(all_bigrams)
   print("Top Keyphrase Frequencies:")
   for phrase, count in counts.most_common(3):
       print(f"  '{phrase}': {count} occurrences")

   Show outputGuess what it prints first

   Top Keyphrase Frequencies:
     'battery life': 3 occurrences
     'great battery': 2 occurrences
     'screen display': 2 occurrences

   Common mistake

   Watch out: Using a list instead of a set for stop words causes O(n) membership lookup overhead on every word in large corpora.

   How you will know

   Output ranks 'battery life' with 3 occurrences as the top customer keyphrase.

5. 05

   Build resilient text analyzer with defensive top-k readout

   In our final milestone, we build an enterprise TextAnalyzer class that integrates tokenization, stop-word filtering, n-gram extraction, and defensive top-k retrieval. The class accepts arbitrary customer review streams, safely absorbs empty or punctuation-only strings without crashing, and produces an executive summary table showing top keyphrases and percentage shares. We run a diverse batch of reviews including an empty string to verify complete resilience against corrupted production text lines and guarantee continuous reporting throughput. This defensive parsing structure ensures that text processing pipelines process arbitrary document streams without throwing unhandled exceptions or corrupting frequency tallies.

   PythonStep 5Copy

   from collections import Counter
   import re
   
   class TextAnalyzer:
       STOP_WORDS = {"the", "is", "and", "but", "in", "to", "a", "of", "it"}
   
       def __init__(self, n: int = 2):
           self.n = n
           self.counter = Counter()
           self.total_docs = 0
   
       def process(self, text: str) -> None:
           self.total_docs += 1
           cleaned = re.sub(r"[^a-zA-Z0-9\s]", " ", text.lower())
           tokens = [w for w in cleaned.split() if w not in self.STOP_WORDS]
           if len(tokens) >= self.n:
               ngrams = [" ".join(tokens[i : i + self.n]) for i in range(len(tokens) - self.n + 1)]
               self.counter.update(ngrams)
   
       def top_phrases(self, k: int = 3) -> list[tuple[str, int]]:
           # Defensive: most_common() returns [] when counter is empty
           return self.counter.most_common(k)
   
   analyzer = TextAnalyzer(n=2)
   batch = [
       "The battery life is amazing. Great battery!",
       "...",  # Degenerate empty review
       "Poor battery life, but great screen display.",
       "Amazing screen display and great battery life."
   ]
   
   for doc in batch:
       analyzer.process(doc)
   
   print(f"Processed {analyzer.total_docs} reviews cleanly.")
   print("Top Phrases Readout:")
   for phrase, freq in analyzer.top_phrases(3):
       print(f"  {phrase} -> {freq}")

   Show outputGuess what it prints first

   Processed 4 reviews cleanly.
   Top Phrases Readout:
     battery life -> 3
     great battery -> 2
     screen display -> 2

   Common mistake

   Watch out: Failing to update total_docs on empty inputs skews document frequency percentages in downstream analytics pipelines.

   How you will know

   Output confirms 4 reviews processed cleanly without crashing, listing 'battery life -> 3' as the top phrase.

Build it yourself

Write your solution below, run it in your browser, and pass every check.

What the result means

Interpretation

The streaming n-gram text analyzer provides complete resilience against malformed, blank, or punctuation-heavy customer reviews. By eliminating the IndexError on empty Counter lookups and filtering stop-words, the system highlights genuine customer complaint topics like 'battery life' while processing batch inputs uninterrupted.

The message you would send

Hi Marketing Insights Team, the overhauled streaming N-Gram text analyzer has been validated. The new pipeline handles empty or malformed reviews gracefully without crashing, accurately isolating top multi-word keyphrases (such as 'battery life' appearing in 75% of sample reviews) for weekly sentiment reporting.

Put it on your CV

Resume or LinkedIn bullet

Engineered a resilient streaming n-gram text analyzer in Python with regex tokenization and defensive Counter indexing, processing 40,000 weekly customer reviews without pipeline crashes.

README for GitHub

# Streaming N-Gram Frequency Text Analyzer

## Business Context
Weekly customer sentiment aggregation pipelines crashed intermittently upon encountering empty reviews or punctuation-only entries, delaying marketing reports by 3 days.

## Solution Architecture
- Applied regex-based alphanumeric tokenization with lowercasing and stop-word filtering.
- Implemented sliding window n-gram extraction to capture contextual customer phrases.
- Protected `collections.Counter` lookups to eliminate `IndexError` exceptions on empty document batches.

## Verification
Passes all automated verification checks on Python 3 standard library.

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