Similarities and Ranking of Documents Using TF-IDF, LDA and WAM

In the era of information abundance, the need for efficient methods to measure document similarity has become paramount. This paper presents a comprehensive exploration of document similarity analysis utilizing Natural Language Processing (NLP) vectorization techniques, cosine similarities, weighted arithmetic mean, and probabilistic approaches. In this paper, TF-IDF to evaluate the importance of a word in a document relative to a collection of documents. LDA is used to discover latent topics within the collection of documents and Weighted Arithmetic Mean is a statistical technique which is used to combine similarities from text datasets. This paper discusses the ways to employ state-of-the-art NLP vectorization methods to represent textual data in a high-dimensional space, capturing semantic relationships between words and documents. The algorithm combines TF-IDF and LDA representations to measure document similarity. The results show the algorithm's capability to identify related documents based on both term importance and latent topics.