What is Topic Modeling?

Topic modeling is an algorithm for extracting the topics of a collection of documents. It is the most widely used text mining method in natural language processing to gain insights about text documents. The algorithm is analogous to dimensional reduction techniques used for numerical data.

In order to understand how words meanings depend on the larger context in which they are used in natural language, a group of text analysis techniques known as “bag analysis” or “group word analysis” examine “bags” or groups of words collectively rather than counting them individually. Topic modeling is one of these methods.

Topic modeling is not the only method that does this– cluster analysis, latent semantic analysis, and other techniques have also been used to identify clustering within texts. A lot can be learned from these approaches. Refer to this article for an interesting discussion of cluster analysis for text.

How Does Topic Modeling Work?

In order to identify subjects within unstructured data, topic modeling involves counting words and grouping words with similar word patterns. Assume you run a software business and are interested in learning what customers think about specific aspects of your offering. You could use a topic modeling algorithm to examine the texts instead of sifting through mountains of comments for passages that mention your areas of interest.

By detecting patterns such as word frequency and distance between words, a topic model clusters feedback that is similar, and words and expressions that appear most often. With this information, end user can quickly deduce what each set of texts are talking about.

Topic Modeling refers to the process of dividing a corpus of documents in two:

1. A list of the topics covered by the documents in the corpus.
2. Several sets of documents from the corpus grouped by the topics they cover.

Different Methods of Topic Modeling:

1. Latent Dirichlet Allocation (LDA): Latent Dirichlet Allocation is a statistical and visual method for finding connections between different papers in a corpus. The Variation Exception Maximization (VEM) technique was used to obtain the greatest probability estimate from the complete text corpus. Traditionally, the best words from the word bag can be chosen to solve this issue. This approach is based on the notion that each given document may be described using each subject and each topic’s probabilistic distribution of words.
2. Latent Semantic Analysis(LSA): Latent Semantic Analysis is another unsupervised learning technique used to identify connections among various words in a collection of documents.This helps us select the necessary documents.It merely serves as a dimensionality reduction technique for the vast corpus of text data.These extraneous data serve as noise when trying to extract the right insights from the data.
3. Non Negative Matrix Factorization: With the NMF matrix factorization technique, one can guarantee that the factorized matrices’ elements are not negative.Take a look at the document-term matrix that was created from a corpus after the stop words were eliminated.The matrix can be factored into the topic-document matrix and the term-topic matrix.Matrix factorization can be carried out using a variety of optimization models.NMF can be carried out more quickly and effectively using hierarchical alternating least squares.
4. Parallel Latent Dirichlet Allocation: Partially labelled Dirichlet allocation is another name for it.Here, the model makes the assumption that there is a set of n labels, each of which is associated with a different topic in the corpus.Then, like the LDA, the individual topics are represented as the probability distribution of the entire corpus.Every document might alternatively have a global subject assigned to it, creating a total of l global topics, where l is the total number of unique documents in the corpus.The approach also makes the assumption that each topic in the corpus has a single label.This procedure is quicker and more accurate than the methods mentioned above because of the labels provided before constructing the model.
5. Pachinko Allocation Model: An enhanced version of the Latent Dirichlet Allocation Model is the Pachinko Allocation Model (PAM). By identifying subjects based on the thematic associations between words found in the corpus, the LDA model highlights the correlation between words.However, PAM improvises by simulating correlations among the generated topics.This approach is more capable of identifying semantic relationships precisely because it also considers how themes relate to one another.The name of the model is taken from the Japanese game Pachinko.The model analyses topic correlation using directed acrylic graphs.DAG is a finite-directed graph to show how the topics are related.

Topic modeling APIs:

1. Open source: While creating a topic modeling solution from scratch, there are numerous open-source libraries accessible. These are great because you can customize them and have complete control over the entire procedure, including data processing, feature extraction, and model training.
2. SaaS APIs: Machine learning is now available as a service, making it easier to use and requiring no programming knowledge. Instead of writing APIs and algorithms, all you have to do is utilize a user-friendly interface to create your machine learning service using your current data.

Challenges of topic modeling:

1. Lack of context: The lack of contextual information in the brief text makes it difficult to identify topics and extract sentiment, which creates a problem with data sparsity. Because they disregard word order and semantic links, broad models like bag-of-words become inappropriate for the semantic analysis of short texts.
2. Need of extensive configuration: The quality of the topic model depends on manipulation and refinement, which are frequently manual and necessitate laborious fine-tuning of model parameters. The problem of setup is one of the biggest obstacles in topic modeling. Data pre-processing should take place before a topic modeling algorithm is performed, and one phase of this procedure entails deleting stop words and subject generic words (TGWs). Typically done manually, topic-general word elimination is difficult and time-consuming.
3. Need for extensive data pre-processing: Effective pre-processing technique selection is regarded as a research priority, and studies have been devoted to the subject. These papers demonstrate how to use comparative analysis techniques to increase the potency of machine learning models that use tweets as input. Writing functions for the filtration of noise from data, setting up the development environment, scaling, and encoding are only a few of the steps involved in data exploration and preparation.

Conclusion:

In contrast to the conventional methods of data reduction used in bioinformatics, topic modeling is a valuable technique that improves researchers’ capacity to analyze biological data. However, the research on topic modeling in biological data still has a long and difficult path ahead of it because there aren’t any topic models that are optimized for particular biological data. Topic models, in our opinion, are a strategy that holds promise for a range of bioinformatics research applications.