Core NLP Processes & Applications

Once text data is ready, the next steps involve core NLP methods—tokenizing, removing stopwords, and generating word clouds to visualize frequency. Learners also practice sentiment analysis for capturing opinions, then extend text mining techniques to real-time scenarios like gathering and interpreting Twitter streams for advanced insights.

CORE NLP CONCEPTS & WORD CLOUDS

Learning Objectives

• Tokenize text by sentences/words, remove stopwords, produce word frequency distributions

• Generate word cloud visualizations to highlight top terms

Indicative Content

• Tokenization

  • nltk.word_tokenize, nltk.sent_tokenize

• Stopwords

  • Removal with nltk.corpus.stopwords

• Word Cloud

  • wordcloud library usage, customizing appearance

SENTIMENT ANALYSIS

Learning Objectives

• Employ dictionary/rule-based sentiment analysis (TextBlob, VADER)

• Interpret polarity in [-1,1] for negative/positive

• Incorporate results into dashboards or feedback loops

Indicative Content

• TextBlob vs. VADER

  • Coverage, social media adaptation

• Sentiment Scores

  • Compound, neg/neu/pos from VADER

• Applications

  • Product reviews, brand sentiment, user feedback

TEXT MINING WITH TWITTER DATA

Learning Objectives

• Obtain tweets programmatically using Twitter API credentials

• Clean text (remove handles, links, punctuation), apply tokenization & sentiment

• Summarize or visualize tweet topics, sentiment distribution in near real-time

Indicative Content

• Twitter Developer Setup

  • API keys/tokens, elevated access

• Tweepy

  • Cursor(api.search_tweets) for searching by keyword, filtering language

• Analysis

  • Word frequencies, word clouds, sentiment classifications

TOOLS & METHODOLOGIES (CORE NLP PROCESSES & APPLICATIONS)

• Python Libraries

  • nltk (tokenizing, stopwords), wordcloud, textblob, nltk.sentiment.vader, tweepy

• Data Flow

  • Ingest raw text (e.g., tweets) → clean (remove handles, punctuation) → tokenize → analyze frequency, sentiment

• Use Cases

  • Brand monitoring, customer feedback classification, real-time event cove