Discover quality document embeddings on my Github repository:

Abstract

Text Mining, a specialized field of Data Mining, involves processing text corpora to analyze content and extract meaningful knowledge. This article examines the quality evaluation of various document embeddings. We will perform clustering on these documents and compare the clustering results using different evaluation metrics.

Exploring Document Embeddings

Paragraph vectors (Doc2Vec) have recently emerged as an unsupervised method for learning document representations. The authors demonstrated the method’s ability to integrate movie reviews for sentiment analysis. While promising, this proof of concept was somewhat limited in scope.

Word embedding has opened up new perspectives through dimensionality reduction and semantic interpretation of words. Similarly, document embedding becomes relevant when dealing with a large volume of documents and aiming for similarity results, classification, and document discrimination techniques (e.g., distinguishing between mechanical and botanical books).

In this article, we expand beyond sentiment analysis by using two additional datasets (Class3 and Reut8) and analysis algorithms like Non-Negative Matrix Factorization (NMF) and Latent Semantic Analysis (LSA). Our goal is to evaluate the quality of their embeddings and assess their performance, strengths, and weaknesses.

Methodology

1. We selected three datasets: movie reviews for sentiment analysis, Class3, and Reut8.
2. Applied NMF, LSA, and Doc2Vec algorithms to generate document embeddings for each dataset.
3. Performed clustering on the embeddings and evaluated the clustering quality using various metrics.
4. Compared the performance, strengths, and weaknesses of each algorithm across the datasets.

Results and Analysis

1. LSA demonstrated faster computation times compared to Doc2Vec and NMF.
2. NMF’s ability to consider the weight of each cluster facilitated easier interpretation of results.
3. Doc2Vec consistently delivered superior results, especially when pre-trained on a large dataset for knowledge transfer (i.e., initializing the neural network’s weights).
4. Despite its effectiveness on classic datasets, Reut8’s flaws could be mitigated by tweaking the Doc2Vec algorithm using two different approaches.

Conclusion

This project provided valuable insights into LSA, NMF, and Doc2Vec methods. Each method has its own strengths and weaknesses, such as LSA’s speed, NMF’s interpretability, and Doc2Vec’s superior performance, particularly with pre-training.

While Doc2Vec showed great promise, Reut8’s flaws could be addressed by adjusting the algorithm. The project utilized several popular Python libraries, enabling diverse ways to analyze and model the datasets.

Although more extensive pre-processing could have improved dataset results, the main focus was on evaluating quality broadly rather than focusing on the specific data context. This project lays the foundation for further exploration and optimization of document embedding techniques. ```