Analyzing Survey Results with LDA Topic Modeling


Survey Monkey


To analyze the results of your survey in Python, you can use a natural language processing (NLP) toolkit such as NLTK or spaCy to preprocess the text data and extract the main topics or themes from the responses. Here’s a general approach you can take:

  1. Preprocess the text data: You can use NLTK or spaCy to tokenize the sentences into individual words, remove stop words, and perform lemmatization or stemming to normalize the words.
  2. Generate word clouds: Word clouds are a simple but effective way to visualize the most common words in the responses. You can use the WordCloud library to generate a word cloud for the responses, with the size of each word indicating its frequency in the responses.
  3. Perform topic modeling: Topic modeling is a machine learning technique that can identify the main topics or themes in a corpus of text. You can use the Gensim library to perform topic modeling on the responses, using algorithms such as Latent Dirichlet Allocation (LDA) or Non-negative Matrix Factorization (NMF). This will identify the most common topics in the responses and the words that are most strongly associated with each topic.
  4. Analyze sentiment: You can use the NLTK SentimentIntensityAnalyzer or a similar library to analyze the sentiment of the responses, and identify whether they are generally positive, negative, or neutral.

By using these techniques, you can gain a better understanding of the main topics and sentiments in the responses to your survey. However, it’s important to keep in mind the limitations of working with a small dataset, as the results may not be generalizable to a larger population. In this article, we will focus on LDA.


Latent Dirichlet Allocation (LDA) is a popular and powerful technique used for topic modeling, which aims to uncover the underlying thematic structure in a collection of documents. LDA has found applications in various fields such as text mining, information retrieval, and natural language processing. In this article, we will explore the basics of LDA, its working principle, and its significance in analyzing large textual datasets.

  1. What is LDA? LDA is a generative probabilistic model that assumes each document consists of a mixture of topics and each topic is a probability distribution over words. The goal of LDA is to learn these latent topics from the document collection without any prior knowledge or labeled data.
  2. How does LDA work? LDA follows a simple yet effective process to discover topics in a document collection. The steps involved are as follows: a. Initialization: Determine the number of topics K to be extracted. b. Document-topic and topic-word assignments: Randomly assign each word in each document to a topic and each topic to a word. c. Iterative estimation: Iterate over the documents and words, adjusting the topic assignments based on the estimated topic distributions and word probabilities. d. Convergence: Repeat the estimation step until the model converges or a predefined stopping criterion is met. e. Inference: Once the model converges, infer the topic distribution for new unseen documents.
  3. Key Concepts in LDA: a. Document-topic distribution: Represents the probability of each topic in a given document. b. Topic-word distribution: Represents the probability of each word in a given topic. c. Dirichlet priors: LDA employs Dirichlet priors to model the distributions and control the sparsity of the topic-word and document-topic distributions. d. Gibbs sampling: A technique used to estimate the hidden variables in LDA by iteratively sampling from their conditional distributions.
  4. Evaluating LDA Models: a. Perplexity: Measures the model’s ability to predict unseen documents. Lower perplexity values indicate better model performance. b. Coherence: Measures the interpretability and coherence of the topics generated by the model. Higher coherence scores indicate more meaningful topics.
  5. Practical Applications of LDA: a. Document categorization: LDA can be used to automatically categorize large collections of documents into meaningful topics, aiding in efficient information retrieval. b. Sentiment analysis: By combining LDA with sentiment analysis techniques, one can identify the sentiment expressed within specific topics. c. Recommender systems: LDA can assist in building recommender systems by extracting user preferences and item-topic associations.

Latent Dirichlet Allocation (LDA) is a valuable tool for uncovering hidden thematic structures within large textual datasets. By identifying latent topics, LDA enables efficient information retrieval, document categorization, sentiment analysis, and recommendation systems. Understanding the principles and applications of LDA empowers researchers and practitioners to extract meaningful insights from vast amounts of textual data and make informed decisions.

In a world inundated with text-based information, LDA stands as a pillar of topic modeling, helping us discover the hidden narratives within words. As we continue to delve deeper into the realm of natural language processing, LDA will undoubtedly remain an essential technique for unraveling the complexity of textual data.


How to design an LDA model:

  1. Preprocess the text data: This involves cleaning the text data by removing stop words, punctuation, and other non-relevant information. You can use libraries like NLTK or spaCy to preprocess the data.
  2. Vectorize the text data: This involves converting the preprocessed text data into a numerical format that can be used for modeling. You can use techniques like bag-of-words or term frequency-inverse document frequency (TF-IDF) to vectorize the text data.
  3. Choose the number of topics: This involves selecting the number of topics you want the algorithm to identify in the data. This is an important step that can greatly affect the quality of the results.
  4. Train the topic model: This involves using an unsupervised machine learning algorithm, such as LDA or NMF, to learn the underlying topics in the text data. You can use libraries like Gensim or Scikit-Learn to train the topic model.
  5. Evaluate the topic model: This involves assessing the quality of the topics identified by the algorithm. You can use metrics such as coherence score, perplexity, or topic diversity to evaluate the model.
  6. Interpret the topics: This involves analyzing the words and phrases associated with each topic to understand the main themes in the data.

Here’s an example code snippet for performing topic modeling using LDA in Python:

import nltk
from nltk.corpus import stopwords
from gensim import corpora, models

# Preprocess the text data
stop_words = set(stopwords.words('english'))
texts = [doc.split() for doc in documents] # documents is a list of text data
texts = [[word for word in doc if word not in stop_words] for doc in texts]
# Vectorize the text data
dictionary = corpora.Dictionary(texts)
corpus = [dictionary.doc2bow(text) for text in texts]
# Train the topic model using LDA
num_topics = 5 # choose the number of topics
lda_model = models.LdaModel(corpus, num_topics=num_topics, id2word=dictionary)
# Evaluate the topic model
coherence_model_lda = models.CoherenceModel(model=lda_model, texts=texts, dictionary=dictionary, coherence='c_v')
coherence_lda = coherence_model_lda.get_coherence()
print('Coherence Score:', coherence_lda)
# Interpret the topics
topics = lda_model.show_topics(num_topics=num_topics, formatted=False)
for topic in topics:
print('Topic {}: {}'.format(topic[0], ' '.join([word[0] for word in topic[1]])))

This code snippet performs topic modeling using LDA on a list of text documents. It uses NLTK to preprocess the text data, Gensim to vectorize and train the topic model, and calculates the coherence score to evaluate the model. Finally, it prints out the main words associated with each topic.


To evaluate the LDA topic model, you can use the following metrics:

  • Coherence Score: The coherence score measures the degree of semantic similarity between the top words in each topic. A higher coherence score indicates that the topics are more coherent and interpretable. The coherence score typically ranges from 0 to 1, with higher values indicating better topic quality.
  • Perplexity: The perplexity measures how well the model predicts new unseen data. It calculates the average log-likelihood of the test documents under the model. A lower perplexity score indicates that the model is better at predicting new data. However, it’s important to note that the absolute value of the perplexity score is not meaningful, and should only be used to compare different models on the same dataset.
  • Topic Distance Diversity (TDD): is a metric used to evaluate the diversity of topics in a topic model. It measures the average distance between topics in a topic model, with a value of 0 indicating that all topics are identical, and a value of 1 indicating that the topics are maximally diverse.

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