How to build a complete end-to-end NLP pipeline using Gensim: topic modeling, word embedding, semantic search, and advanced text analysis
In this tutorial, we propose a complete end-to-end natural language processing (NLP) pipeline Gensim and a supported library designed to run seamlessly in Google Colab. It integrates multiple core technologies into modern NLP, including preprocessing, theme modeling and potential dirichlet allocation (LDA), Word embedding for Word2Vec, similarity analysis based on TF-IDF, and semantic search. The pipeline not only demonstrates how these models can be trained and evaluated, but also demonstrates practical visualization, advanced topic analysis, and document classification workflows. By combining statistical methods with machine learning methods, the tutorial provides a comprehensive framework for understanding and experimenting with text data. Check The complete code is here.
!pip install --upgrade scipy==1.11.4
!pip install gensim==4.3.2 nltk wordcloud matplotlib seaborn pandas numpy scikit-learn
!pip install --upgrade setuptools
print("Please restart runtime after installation!")
print("Go to Runtime > Restart runtime, then run the next cell")
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from wordcloud import WordCloud
import warnings
warnings.filterwarnings('ignore')
from gensim import corpora, models, similarities
from gensim.models import Word2Vec, LdaModel, TfidfModel, CoherenceModel
from gensim.parsing.preprocessing import preprocess_string, strip_tags, strip_punctuation, strip_multiple_whitespaces, strip_numeric, remove_stopwords, strip_short
import nltk
nltk.download('punkt', quiet=True)
nltk.download('stopwords', quiet=True)
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenizeWe install and upgrade necessary libraries such as Scipy, Gensim, NLTK and visualization tools to ensure compatibility. We then import all the required modules for preprocessing, modeling and analysis. We also downloaded NLTK Resources to effectively reference and process stop words, thus setting up the environment for our NLP pipeline. Check The complete code is here.
class AdvancedGensimPipeline:
def __init__(self):
self.dictionary = None
self.corpus = None
self.lda_model = None
self.word2vec_model = None
self.tfidf_model = None
self.similarity_index = None
self.processed_docs = None
def create_sample_corpus(self):
"""Create a diverse sample corpus for demonstration"""
documents = [ "Data science combines statistics, programming, and domain expertise to extract insights",
"Big data analytics helps organizations make data-driven decisions at scale",
"Cloud computing provides scalable infrastructure for modern applications and services",
"Cybersecurity protects digital systems from threats and unauthorized access attempts",
"Software engineering practices ensure reliable and maintainable code development",
"Database management systems store and organize large amounts of structured information",
"Python programming language is widely used for data analysis and machine learning",
"Statistical modeling helps identify patterns and relationships in complex datasets",
"Cross-validation techniques ensure robust model performance evaluation and selection",
"Recommendation systems suggest relevant items based on user preferences and behavior",
"Text mining extracts valuable insights from unstructured textual data sources",
"Image classification assigns predefined categories to visual content automatically",
"Reinforcement learning trains agents through interaction with dynamic environments"
]
return documents
def preprocess_documents(self, documents):
"""Advanced document preprocessing using Gensim filters"""
print("Preprocessing documents...")
CUSTOM_FILTERS = [
strip_tags, strip_punctuation, strip_multiple_whitespaces,
strip_numeric, remove_stopwords, strip_short, lambda x: x.lower()
]
processed_docs = []
for doc in documents:
processed = preprocess_string(doc, CUSTOM_FILTERS)
stop_words = set(stopwords.words('english'))
processed = [word for word in processed if word not in stop_words and len(word) > 2]
processed_docs.append(processed)
self.processed_docs = processed_docs
print(f"Processed {len(processed_docs)} documents")
return processed_docs
def create_dictionary_and_corpus(self):
"""Create Gensim dictionary and corpus"""
print("Creating dictionary and corpus...")
self.dictionary = corpora.Dictionary(self.processed_docs)
self.dictionary.filter_extremes(no_below=2, no_above=0.8)
self.corpus = [self.dictionary.doc2bow(doc) for doc in self.processed_docs]
print(f"Dictionary size: {len(self.dictionary)}")
print(f"Corpus size: {len(self.corpus)}")
def train_word2vec_model(self):
"""Train Word2Vec model for word embeddings"""
print("Training Word2Vec model...")
self.word2vec_model = Word2Vec(
sentences=self.processed_docs,
vector_size=100,
window=5,
min_count=2,
workers=4,
epochs=50
)
print("Word2Vec model trained successfully")
def analyze_word_similarities(self):
"""Analyze word similarities using Word2Vec"""
print("n=== Word2Vec Similarity Analysis ===")
test_words = ['machine', 'data', 'learning', 'computer']
for word in test_words:
if word in self.word2vec_model.wv:
similar_words = self.word2vec_model.wv.most_similar(word, topn=3)
print(f"Words similar to '{word}': {similar_words}")
try:
if all(w in self.word2vec_model.wv for w in ['machine', 'computer', 'data']):
analogy = self.word2vec_model.wv.most_similar(
positive=['computer', 'data'],
negative=['machine'],
topn=1
)
print(f"Analogy result: {analogy}")
except:
print("Not enough vocabulary for complex analogies")
def train_lda_model(self, num_topics=5):
"""Train LDA topic model"""
print(f"Training LDA model with {num_topics} topics...")
self.lda_model = LdaModel(
corpus=self.corpus,
id2word=self.dictionary,
num_topics=num_topics,
random_state=42,
passes=10,
alpha="auto",
per_word_topics=True,
eval_every=None
)
print("LDA model trained successfully")
def evaluate_topic_coherence(self):
"""Evaluate topic model coherence"""
print("Evaluating topic coherence...")
coherence_model = CoherenceModel(
model=self.lda_model,
texts=self.processed_docs,
dictionary=self.dictionary,
coherence="c_v"
)
coherence_score = coherence_model.get_coherence()
print(f"Topic Coherence Score: {coherence_score:.4f}")
return coherence_score
def display_topics(self):
"""Display discovered topics"""
print("n=== Discovered Topics ===")
topics = self.lda_model.print_topics(num_words=8)
for idx, topic in enumerate(topics):
print(f"Topic {idx}: {topic[1]}")
def create_tfidf_model(self):
"""Create TF-IDF model for document similarity"""
print("Creating TF-IDF model...")
self.tfidf_model = TfidfModel(self.corpus)
corpus_tfidf = self.tfidf_model[self.corpus]
self.similarity_index = similarities.MatrixSimilarity(corpus_tfidf)
print("TF-IDF model and similarity index created")
def find_similar_documents(self, query_doc_idx=0):
"""Find documents similar to a query document"""
print(f"n=== Document Similarity Analysis ===")
query_doc_tfidf = self.tfidf_model[self.corpus[query_doc_idx]]
similarities_scores = self.similarity_index[query_doc_tfidf]
sorted_similarities = sorted(enumerate(similarities_scores), key=lambda x: x[1], reverse=True)
print(f"Documents most similar to document {query_doc_idx}:")
for doc_idx, similarity in sorted_similarities[:5]:
print(f"Doc {doc_idx}: {similarity:.4f}")
def visualize_topics(self):
"""Create visualizations for topic analysis"""
print("Creating topic visualizations...")
doc_topic_matrix = []
for doc_bow in self.corpus:
doc_topics = dict(self.lda_model.get_document_topics(doc_bow, minimum_probability=0))
topic_vec = [doc_topics.get(i, 0) for i in range(self.lda_model.num_topics)]
doc_topic_matrix.append(topic_vec)
doc_topic_df = pd.DataFrame(doc_topic_matrix, columns=[f'Topic_{i}' for i in range(self.lda_model.num_topics)])
plt.figure(figsize=(12, 8))
sns.heatmap(doc_topic_df.T, annot=True, cmap='Blues', fmt=".2f")
plt.title('Document-Topic Distribution Heatmap')
plt.xlabel('Documents')
plt.ylabel('Topics')
plt.tight_layout()
plt.show()
fig, axes = plt.subplots(2, 3, figsize=(15, 10))
axes = axes.flatten()
for topic_id in range(min(6, self.lda_model.num_topics)):
topic_words = dict(self.lda_model.show_topic(topic_id, topn=20))
wordcloud = WordCloud(
width=300, height=200,
background_color="white",
colormap='viridis'
).generate_from_frequencies(topic_words)
axes[topic_id].imshow(wordcloud, interpolation='bilinear')
axes[topic_id].set_title(f'Topic {topic_id}')
axes[topic_id].axis('off')
for i in range(self.lda_model.num_topics, 6):
axes[i].axis('off')
plt.tight_layout()
plt.show()
def advanced_topic_analysis(self):
"""Perform advanced topic analysis"""
print("n=== Advanced Topic Analysis ===")
topic_distributions = []
for i, doc_bow in enumerate(self.corpus):
doc_topics = self.lda_model.get_document_topics(doc_bow)
dominant_topic = max(doc_topics, key=lambda x: x[1]) if doc_topics else (0, 0)
topic_distributions.append({
'doc_id': i,
'dominant_topic': dominant_topic[0],
'topic_probability': dominant_topic[1]
})
topic_df = pd.DataFrame(topic_distributions)
plt.figure(figsize=(10, 6))
topic_counts = topic_df['dominant_topic'].value_counts().sort_index()
plt.bar(range(len(topic_counts)), topic_counts.values)
plt.xlabel('Topic ID')
plt.ylabel('Number of Documents')
plt.title('Distribution of Dominant Topics Across Documents')
plt.xticks(range(len(topic_counts)), [f'Topic {i}' for i in topic_counts.index])
plt.show()
return topic_df
def document_classification_demo(self, new_document):
"""Classify a new document using trained models"""
print(f"n=== Document Classification Demo ===")
print(f"Classifying: '{new_document[:50]}...'")
processed_new = preprocess_string(new_document, [
strip_tags, strip_punctuation, strip_multiple_whitespaces,
strip_numeric, remove_stopwords, strip_short, lambda x: x.lower()
])
new_doc_bow = self.dictionary.doc2bow(processed_new)
doc_topics = self.lda_model.get_document_topics(new_doc_bow)
print("Topic probabilities:")
for topic_id, prob in doc_topics:
print(f" Topic {topic_id}: {prob:.4f}")
new_doc_tfidf = self.tfidf_model[new_doc_bow]
similarities_scores = self.similarity_index[new_doc_tfidf]
most_similar = np.argmax(similarities_scores)
print(f"Most similar document: {most_similar} (similarity: {similarities_scores[most_similar]:.4f})")
return doc_topics, most_similar
def run_complete_pipeline(self):
"""Execute the complete NLP pipeline"""
print("=== Advanced Gensim NLP Pipeline ===n")
raw_documents = self.create_sample_corpus()
self.preprocess_documents(raw_documents)
self.create_dictionary_and_corpus()
self.train_word2vec_model()
self.train_lda_model(num_topics=5)
self.create_tfidf_model()
self.analyze_word_similarities()
coherence_score = self.evaluate_topic_coherence()
self.display_topics()
self.visualize_topics()
topic_df = self.advanced_topic_analysis()
self.find_similar_documents(query_doc_idx=0)
new_doc = "Deep neural networks are powerful machine learning models for pattern recognition"
self.document_classification_demo(new_doc)
return {
'coherence_score': coherence_score,
'topic_distributions': topic_df,
'models': {
'lda': self.lda_model,
'word2vec': self.word2vec_model,
'tfidf': self.tfidf_model
}
}We define the AdvancedGensImpiPeline class as a modular framework to handle each stage of text analysis in one place. It first creates the sample corpus, preprocesses it, and then builds the dictionary and corpus representation. We use Word2Vec training for embedding, LDA for topic modeling and TF-IDF for similarity, followed by visualization, coherence evaluation, and classification of new documents. This way, we summarize the complete NLP workflow (from original text to insights) into a reusable pipeline. Check The complete code is here.
def compare_topic_models(pipeline, topic_range=[3, 5, 7, 10]):
print("n=== Topic Model Comparison ===")
coherence_scores = []
perplexity_scores = []
for num_topics in topic_range:
lda_temp = LdaModel(
corpus=pipeline.corpus,
id2word=pipeline.dictionary,
num_topics=num_topics,
random_state=42,
passes=10,
alpha="auto"
)
coherence_model = CoherenceModel(
model=lda_temp,
texts=pipeline.processed_docs,
dictionary=pipeline.dictionary,
coherence="c_v"
)
coherence = coherence_model.get_coherence()
coherence_scores.append(coherence)
perplexity = lda_temp.log_perplexity(pipeline.corpus)
perplexity_scores.append(perplexity)
print(f"Topics: {num_topics}, Coherence: {coherence:.4f}, Perplexity: {perplexity:.4f}")
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5))
ax1.plot(topic_range, coherence_scores, 'bo-')
ax1.set_xlabel('Number of Topics')
ax1.set_ylabel('Coherence Score')
ax1.set_title('Model Coherence vs Number of Topics')
ax1.grid(True)
ax2.plot(topic_range, perplexity_scores, 'ro-')
ax2.set_xlabel('Number of Topics')
ax2.set_ylabel('Perplexity')
ax2.set_title('Model Perplexity vs Number of Topics')
ax2.grid(True)
plt.tight_layout()
plt.show()
return coherence_scores, perplexity_scoresThis feature comparison _topic_models allows us to systematically test different numbers of topics of LDA models and compare their performance. We compute consistency scores (check topic interpretability) and model scores for each topic count within a given range (check model fit). The results are shown as row graphs, helping us visually determine the most balanced number of datasets. Check The complete code is here.
def semantic_search_engine(pipeline, query, top_k=5):
"""Implement semantic search using trained models"""
print(f"n=== Semantic Search: '{query}' ===")
processed_query = preprocess_string(query, [
strip_tags, strip_punctuation, strip_multiple_whitespaces,
strip_numeric, remove_stopwords, strip_short, lambda x: x.lower()
])
query_bow = pipeline.dictionary.doc2bow(processed_query)
query_tfidf = pipeline.tfidf_model[query_bow]
similarities_scores = pipeline.similarity_index[query_tfidf]
top_indices = np.argsort(similarities_scores)[::-1][:top_k]
print("Top matching documents:")
for i, idx in enumerate(top_indices):
score = similarities_scores[idx]
print(f"{i+1}. Document {idx} (Score: {score:.4f})")
print(f" Content: {' '.join(pipeline.processed_docs[idx][:10])}...")
return top_indices, similarities_scores[top_indices]The Semantic_search_engine function adds a search layer to the pipeline by querying, preprocessing and converting it into a bag of words and TF-IDF representation. It then uses a similarity index to compare the query to all documents and returns a top-level match. This way, we can quickly retrieve the most relevant documents and their similarity scores, making the pipeline available for real-world information retrieval and semantic search tasks. Check The complete code is here.
if __name__ == "__main__":
pipeline = AdvancedGensimPipeline()
results = pipeline.run_complete_pipeline()
print("n" + "="*60)
coherence_scores, perplexity_scores = compare_topic_models(pipeline)
print("n" + "="*60)
search_results = semantic_search_engine(
pipeline,
"artificial intelligence neural networks deep learning"
)
print("n" + "="*60)
print("Pipeline completed successfully!")
print(f"Final coherence score: {results['coherence_score']:.4f}")
print(f"Vocabulary size: {len(pipeline.dictionary)}")
print(f"Word2Vec model size: {pipeline.word2vec_model.wv.vector_size} dimensions")
print("nModels trained and ready for use!")
print("Access models via: pipeline.lda_model, pipeline.word2vec_model, pipeline.tfidf_model")This main block ties everything together and divides it into a complete executable pipeline. We initialize the advanced gensimple, run the complete workflow, and then evaluate the topic model with different number of topics. Next, we test semantic search engines with queries about artificial intelligence and deep learning. Finally, it prints out summary metrics such as coherent scores, vocabulary sizes, and Word2Vec embed sizes, confirming that all models are trained and ready for further use.
In conclusion, we obtain a powerful modular workflow covering the entire scope of text analysis, from cleaning and preprocessing original documents to discovering hidden topics, visualizing results, comparing models, and performing semantic searches. The inclusion of Word2Vec embedding, TF-IDF similarity and coherence evaluation ensures the versatility and stability of the pipeline, while visualization and classification demonstrations make the results interpretable and actionable. This cohesive design enables learners, researchers and practitioners to quickly adapt to the framework of real-world applications, thus becoming an invaluable foundation for advanced NLP experiments and text analysis that can provide production.
Check The complete code is here. Check out ours anytime Tutorials, codes and notebooks for github pages. Also, please stay tuned for us twitter And don’t forget to join us 100K+ ml reddit And subscribe Our newsletter.
Asif Razzaq is CEO of Marktechpost Media Inc. As a visionary entrepreneur and engineer, ASIF is committed to harnessing the potential of artificial intelligence to achieve social benefits. His recent effort is to launch Marktechpost, an artificial intelligence media platform that has an in-depth coverage of machine learning and deep learning news that can sound both technically, both through technical voices and be understood by a wide audience. The platform has over 2 million views per month, demonstrating its popularity among its audience.
