Text Mining Guide
A skill for applying natural language processing (NLP) and text mining techniques to research data. Covers text preprocessing, feature extraction, topic modeling, sentiment analysis, and named entity recognition for analyzing surveys, abstracts, social media, and document corpora.
Text Preprocessing Pipeline
Standard Cleaning Steps
pythonimport re from collections import Counter def preprocess_text(text: str, lowercase: bool = True, remove_numbers: bool = False, min_word_length: int = 2) -> list[str]: """ Preprocess text for NLP analysis. Args: text: Raw input text lowercase: Convert to lowercase remove_numbers: Remove numeric tokens min_word_length: Minimum token length to keep """ if lowercase: text = text.lower() # Remove URLs text = re.sub(r"http\S+|www\.\S+", "", text) # Remove HTML tags text = re.sub(r"<[^>]+>", "", text) # Remove special characters (keep apostrophes for contractions) text = re.sub(r"[^a-zA-Z0-9\s']", " ", text) # Tokenize tokens = text.split() if remove_numbers: tokens = [t for t in tokens if not t.isdigit()] # Remove short tokens tokens = [t for t in tokens if len(t) >= min_word_length] return tokens def remove_stopwords(tokens: list[str], custom_stopwords: list[str] = None) -> list[str]: """ Remove stopwords from token list. """ # Minimal English stopwords (extend as needed) default_stops = { "the", "a", "an", "and", "or", "but", "in", "on", "at", "to", "for", "of", "with", "by", "is", "was", "are", "were", "be", "been", "being", "have", "has", "had", "do", "does", "did", "will", "would", "could", "should", "may", "might", "this", "that", "these", "those", "it", "its", "not", "no" } if custom_stopwords: default_stops.update(custom_stopwords) return [t for t in tokens if t not in default_stops]
Document-Term Matrix
pythonfrom sklearn.feature_extraction.text import TfidfVectorizer def build_tfidf_matrix(documents: list[str], max_features: int = 5000) -> dict: """ Build a TF-IDF document-term matrix. Args: documents: List of document strings max_features: Maximum vocabulary size """ vectorizer = TfidfVectorizer( max_features=max_features, stop_words="english", min_df=2, # Appear in at least 2 documents max_df=0.95, # Ignore terms in >95% of documents ngram_range=(1, 2) # Unigrams and bigrams ) tfidf_matrix = vectorizer.fit_transform(documents) return { "matrix_shape": tfidf_matrix.shape, "vocabulary_size": len(vectorizer.vocabulary_), "top_terms": sorted( vectorizer.vocabulary_.items(), key=lambda x: x[1] )[:20], "vectorizer": vectorizer, "matrix": tfidf_matrix }
Topic Modeling
Latent Dirichlet Allocation (LDA)
pythonfrom sklearn.decomposition import LatentDirichletAllocation def run_topic_model(tfidf_matrix, vectorizer, n_topics: int = 10) -> list[dict]: """ Run LDA topic modeling on a document-term matrix. Args: tfidf_matrix: Sparse TF-IDF matrix vectorizer: Fitted TfidfVectorizer n_topics: Number of topics to extract """ lda = LatentDirichletAllocation( n_components=n_topics, random_state=42, max_iter=50, learning_method="online" ) lda.fit(tfidf_matrix) feature_names = vectorizer.get_feature_names_out() topics = [] for idx, topic_weights in enumerate(lda.components_): top_indices = topic_weights.argsort()[-10:][::-1] top_words = [feature_names[i] for i in top_indices] topics.append({ "topic_id": idx, "top_words": top_words, "label": "Assign a human-readable label based on top words" }) return topics
Choosing the Number of Topics
Methods for selecting k (number of topics): - Coherence score: Higher is better (use gensim's CoherenceModel) - Perplexity: Lower is better (but can overfit) - Human judgment: Do topics make interpretive sense? - Domain knowledge: Expected number of themes in the corpus Practical advice: - Start with k = 5, 10, 15, 20 and compare - Examine top words for each k -- look for coherent themes - If topics are too broad, increase k - If topics overlap heavily, decrease k
Sentiment Analysis
Lexicon-Based Approach
pythondef simple_sentiment(text: str, positive_words: set, negative_words: set) -> dict: """ Basic lexicon-based sentiment scoring. Args: text: Input text positive_words: Set of positive sentiment words negative_words: Set of negative sentiment words """ tokens = text.lower().split() pos_count = sum(1 for t in tokens if t in positive_words) neg_count = sum(1 for t in tokens if t in negative_words) total = len(tokens) score = (pos_count - neg_count) / max(total, 1) return { "positive_count": pos_count, "negative_count": neg_count, "score": score, "label": ( "positive" if score > 0.05 else "negative" if score < -0.05 else "neutral" ) }
Research Applications
Common Text Mining Tasks in Research
| Task | Method | Application |
|---|---|---|
| Literature mapping | Topic modeling | Identify research themes in a corpus of abstracts |
| Survey analysis | Thematic coding + sentiment | Analyze open-ended survey responses |
| Social media analysis | NER + sentiment | Track public discourse on a topic |
| Content analysis | Classification + keyword extraction | Code qualitative data at scale |
| Bibliometrics | Co-word analysis | Map intellectual structure of a field |
Validation and Reporting
Always validate text mining results against human judgment. Report preprocessing steps, parameter choices (e.g., number of topics, min_df, max_df), and model evaluation metrics. For topic models, include the top 10-15 words per topic and representative documents. For classification, report precision, recall, and F1 on a held-out test set. Acknowledge that automated text analysis supplements but does not replace close reading.
