Extracción de Términos Clave y Modelado de Temas

Latent theme analysis (LDA) is a powerful probabilistic tool for uncovering hidden patterns in large textual data sets. This technique is especially valuable for understanding trends and opinions expressed in product or service reviews, offering deeper insights than simple keyword analysis. Next, we will explore how to implement LDA to classify hidden topics in datasets and visualize word vector spaces, opening up new possibilities for sentiment analysis.

How does latent topic analysis (LDA) work?

LDA (Latent Dirichlet Allocation) is a probabilistic technique that allows us to detect and classify hidden topics throughout a text dataset. Unlike simpler methods, LDA considers the relationships between words and documents to identify underlying thematic patterns.

To implement LDA in our review analysis, we need to combine this technique with keyword extraction methods, which can be based on TFIDF (Term Frequency-Inverse Document Frequency) or backoff word models, as we saw earlier. In the example code, we use SiteKiller with this method to search for five main topics.

An important aspect to consider is the configuration of the random_state parameter (in this case, set to 42) to guarantee determinism in the results, which allows us to obtain the same topics each time we run the algorithm.

# Example of LDA implementation# We search for top 5 topics with random_state=42 for determinism# The process can take several minutes (approximately 7:35 in the example).

The result of this process provides us with a classification of hidden themes that appear throughout all the reviews, allowing us to identify patterns such as "quality", "good price", "good product", "very nice" and "perfect color", among others.

How to visualize word vector spaces?

One of the most fascinating capabilities of modern natural language processing is the ability to visualize words as vectors in a multidimensional space. This representation makes it possible to capture semantic relationships between words that go beyond their orthographic similarity.

In the example presented, a dataset called "word to Beck" is used, where each word is converted into a 200-dimensional vector. To visualize this multidimensional space in a comprehensible way, dimensionality reduction methods are employed that transform these 200-dimensional vectors into three-dimensional representations.

What is really relevant about this visualization is how it captures semantic relationships. For example, if we select the word "king", we find that its nearest neighbor is "queen". This shows that, although these words are spelled differently, they are close in semantic space because of their conceptual relationship.

What practical applications do these embeddings have?

Word embeddings allow us to:

• Identify semantic relationships between concepts
• Improve search and recommendation systems
• Facilitate the understanding of contexts in sentiment analysis
• Enhance machine translation models

Readers are invited to experiment with Project Embedding, testing words such as "dog" to discover which other words are semantically close in vector space.

What benefits does LDA bring to review analysis?

Implementing LDA for review analysis offers numerous advantages over more traditional methods:

• Discovery of non-obvious themes: Identifies thematic patterns that might go unnoticed in a cursory analysis.
• Unsupervised analysis: Does not require predefined labels, allowing the data to reveal its own structures.
• Contextual understanding: Considers relationships between words, not just their frequency.
• Foundation for advanced systems: Provides a foundation for creating more sophisticated sentiment classifiers.

In the example shown, the LDA process took approximately 7 minutes and 35 seconds, after which the identified topics could be visualized. Although the results may initially seem strange due to the previously applied lemmatization process, it is possible to identify clear thematic relationships such as "quality", "good price", "good product", among others.

The analysis of latent themes represents a fundamental step to understand in depth the opinions expressed in reviews. This technique, combined with the visualization of word vector spaces, allows us to extract valuable information that goes beyond what is explicitly mentioned. In the next steps, these fundamentals will be used to create sentiment classifiers and ratings from scratch, taking our analysis to a higher level. Have you ever used LDA techniques in your projects? Share your experiences and results with Project Embedding in the comments.