How to create a complete NLP pipe with gensim: Modeling title, word prevention, semantic searches, and advanced text analysis
In this study, we introduce a complete complete pipe to end the language (NLP) Hensim and to support libraries, it is designed to run outside of the seams in Google Colab. It includes many modern NLP strategies, including pre-performance, the title of the Latent Dirichlet Allocation (LDA), Word incorporation, the same IDF search. The pipe is not the only training and evaluation of these models but also indicates practical recognition, analysis of an advanced topic, and the transaction of the Scriptural separation work. By combining mathematical methods, the lesson provides a comprehensive framework for understanding and trying with text data on a scale. Look Full codes 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 also add and improve the required libraries, such as Scripy, gensim, nltk, and visual tools, to ensure compliance. We have introduced all the necessary modules to gain energy, modeling, and analysis. We also downloaded NTLTK resources to use the Tokotoma and manage the correct identifiers, thus suspension of the NLP pipeline. Look Full codes 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 describe the AdventrTrimplicine class as a dynamic framework to handle all text analysis phase in one place. It begins with the sample corpus, it is the priority, and forms dictionaries and presenting presentations. We are training the Word2vect of embedding, LDA TDA Modeling, and TF-IDF similarities, followed by eye contact, and testing new documents. In this way, we bring together the NLP's comprehensive NLP work, from an unripe text to Insights, one practical pudget. Look Full codes 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 work compares to_topic_models allows us in order to check in order to separate the LDA model subjects and compare their performance. We calculate coreener scores (looking at the topic of the title) and frustrated scores (looking at the model Fit) for each subject provided. The results are shown as line zones, helps us decide by monitoring a balanced amount of topics of our data. Look Full codes 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]Semantic_search_Ne Engine added a pipe layer by taking a question, found it, and converted it into the Fund and TF-IDFs. Then it compares the question that fights all the documents using the same index and returns higher games. In this way, we can quickly find the most appropriate documents and the same scores, making the pipeline useful for effective returns and semantic search operations. Look Full codes 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 into a complete and unlimited pipe. We start Adventgensgencympori, run full of work, and check the title models in different numbers of the topics. Next, we test the Semantic Search engine with a question about the Intelligence for artificial and deep reading. Finally, printing metrics summed, such as a symbol, the size of the names, and the size of the words, which ensures that all the models are trained and ready for other use.
In conclusion, we receive powerful, variable movement that includes the entire scripture, cleaning and cleaning scripts to find hidden headings, to find out the results, and to make semantic search. Introduction of Word2VECs, Simple TF-IDF, and testing Cotries ensures that the pipe is variable and firm, while the demolitions make the results that make results and are not available. The co-ordinating project is performing students, researchers, and doctors to quickly adopt the outline of the world's actual planning systems, which makes it an important basis for advanced NLP analysis and the appropriate analysis.
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