Como você usa a modelagem de tópicos para resumo, classificação ou agrupamento de texto?

Another example is customer feedback on products and services, which can have multiple topics ranging from service received, to the problem encountered, to wait time on the call. It is helpful to understand the feedback topics so solutions can be quickly created.

Topic modeling -unsupervised learning helps to find the hidden patterns and structures in the text data. -Summarize:LDA for probabilistic topic-word assignments, extracting key topics and words. -BERTopic for richer semantic understanding. -Classify:Analyze topic distributions within documents use LDA for theme identification&categorization.Use embeddings like BERT. -clustering:Group similar documents by measuring document similarity with LDA.LSA for effective clustering by reducing dimensionality and identifying clusters based on topic vector similarities. -LDA,NMF,LSA offer probabilistic modeling, matrix factorization and dimensionality reduction. -Gensim,Scikit-learn,MALLET provide topic modeling algorithms, preprocessing, evaluation...

You can find a summary I did on topic modeling and its main models in this article: https://www.linkedin.com/pulse/topic-modelling-methods-comparison-hosna-hamdieh/?trackingId=qAzXk6tWRF6PP1B1NmvrbA%3D%3D Or another one I have published in my professional page (I4Data): https://www.linkedin.com/pulse/nlp-topic-modeling-short-i4data/?published=t

In the vector space model(VSM), each word is considered as a independent unit. For example, according to VSM, the word "bank" does not have any relationship with the word "finance" and the word "river". But, in topic modeling word relationship is identified by their co-occurrence. For example, if "bank" is present in financial documents, then "bank" would be mapped along with finance topics. Otherwise, word "bank" would be mapped with river topic. Latent Dirichlet Allocation(LDA) is a popular topic modeling algorithm. It is a probabilistic generative modeling, where probability distribution is used to generate topics from documents. LDA algorithm identifies word that belong to the document and probability of word belonging to a topic.

Topic modeling is a technique used in natural language processing to uncover hidden thematic structures within a collection of documents. It aims to identify topics or themes that frequently co-occur in the text corpus. The process involves analyzing the distribution of words across documents to group them into topics, where each topic represents a set of words that are likely to occur together. By doing so, topic modeling helps in understanding the underlying themes and patterns present in large volumes of text data, enabling tasks such as document organization, summarization, and information retrieval.

As another example, we can also use topic modeling to label data. This is something I did on the job project to label text files with the purpose to create training data.

Imagine a library with thousands of books, and you need a quick gist of each section. Instead of reading every page, topic modeling, like LDA, acts as a librarian that identifies common themes in each section. By understanding these themes, one can extract the 'heart' of the texts. For instance, if LDA identifies 'space', 'planets', and 'stars' as dominant topics, the summary might be about astronomy. It's a method to glimpse into vast textual universes swiftly.

Latent Dirichlet Allocation(LDA) and Singular value decomposition(SVD) are two popular algorithms which are used for topic modeling. These algorithms can be used for summarization in different ways. LDA algorithm is used to identify mixture of topics. Hence, some paragraph can have multiple topics and some paragraph does not contain any topic. It can identify the relevancy of paragraph on the basis of topic occurrence. Whereas SVD can be used for dimensionality reduction. SVD uses matrix factorization, where we can find top words using matrix rank operation. The top words can be treated as topic. It can also detect relationship of these words with documents or document's segments. On the basis this relationship summary can be generated.

Topic modeling is used in text summarization to identify key topics in a document or set of documents, extract relevant sentences related to these topics, and generate a concise summary. This method, often based on techniques like Latent Dirichlet Allocation (LDA) or Non-negative Matrix Factorization (NMF), helps condense large volumes of text by preserving essential content while reducing redundancy and noise.

When it comes to using Topic Modeling for text Classification, I can think of two areas- 1. Feature Engineering: Topic distributions can serve as features for the classification model. If we use LDA on a set of documents, each document will be represented as a distribution over topics. These distributions can be used as input features for a classifier. 2. Semi-supervised Learning: In cases where labeled data is small, topic modeling can be used to explore the underlying themes in the data, and this understanding can be leveraged to guide the classification process.

Topic modeling can be used for classification in a no. of ways. Topic modeling algorithm can be used to label document based on the extracted topic from document. It can also be used for creating taxonomies from the documents. Later, taxonomy can be used for text classification. In Text classification, words which are present in the document are treated as features. Topic Modeling algorithm like SVD algo can be used for dimensionality reduction. Where we can identify top-K words present in the document. We can use topic vector which is extracted from SVD for classification. As compared to SVD, LDA generates sparse topic vector, so it cannot be directly used. Apart from that algorithms like labelled LDA can be used for classification.

Topic modeling can be integrated into an active learning framework to selectively sample documents for annotation to improve the classification model. First, we calculate the topic distributions of documents to estimate their representativeness or informativeness for the classification task. Documents with uncertain or diverse topic distributions can then be selected for manual annotation to update the model and improve its accuracy.

Topic modeling can be employed for text classification by representing documents as distributions over topics. Each document is assigned a probability distribution across different topics, and these distributions are then used as features for classification. Techniques like Latent Dirichlet Allocation (LDA) or Latent Semantic Analysis (LSA) can be applied to extract topics from the documents, and the resulting topic distributions are used as input to machine learning algorithms for classification. This approach allows for capturing the underlying themes or topics in the text, enabling more effective classification based on semantic content rather than just keywords or phrases.

It greatly depends on supervised/un supervised or clean/noisy text, length of the text etc., matters before picking any model. I have seen if data is clean and good, simple logistic regression also works great with 85% accuracy for complex data - Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), K-Means can be used, which are unsupervised. - You can also use simple heuristics like Term Frequency-Inverse Document Frequency (TF-IDF), which is unsupervised but often combined with supervised classifiers. - On the advanced end BERT, AlBert, BERTopic is another one utilising the concept of TF-IDF as well, giving importance to input features etc., can be used for supervised classification tasks.

Think of topic modeling as a keen-eyed botanist who can detect underlying patterns in a vast forest. By identifying shared topics, like the common trees or plants, this botanist can determine which areas of the forest are alike. Using LSA, our 'botanist' discerns the latent themes in each blog post, akin to sensing the similar flora of different forest patches. When you cluster using these themes, it's like grouping forest regions by predominant vegetation, revealing the landscape's structure.

Common topic modeling algorithms and tools like LDA, NMF, and LSA, along with libraries such as Gensim and scikit-learn, offer efficient ways to extract meaningful topics from text data.

Thanks to Maarten Grootendorst for the introduction of BERTopic as a modular topic model. I am using this in my project and very productie.

BERTopic is a solid choice for unsupervised topic modeling, particularly if you're working with a smaller, niche dataset. Just be mindful that tweaking the settings can really change the output, sometimes dramatically increasing the number of topics. Also, the keyword format of the results might not be as intuitive for domain experts as the kind of insights you get from supervised learning. Unsupervised learning has that 'wow' factor of uncovering hidden patterns, but you'll likely need to help your audience make sense of it.

Unlike extractive NLP methods which are purely lexically based (keywords), topic modelling tries to capture underlying structure and meaning in documents i.e. semantics. The classical technique is Latent Dirichlet Allocation (LDA), which generates word and topic distributions from the Dirichlet density function (based on minimising a cost function). Modern techniques use embeddings to cluster both words and documents into the same vector space e.g. BERTopic (which uses BERT embeddings). A novel approach is to use LLMs to generate human readable concepts from topic words generated by topic models (either LDA or BERTopic).