Sep 20, 2023
Unlock insights and streamline content with powerful topic modeling techniques (Contact US) https://www.aqbsolutions.com/contact-us/
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Unlock insights and streamline content with powerful topic modeling techniques (Contact US) https://www.aqbsolutions.com/contact-us/
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Topics interlinkage matrix
Word clouds of Topics
See how to do topic modeling using Roberta and transformers. We will use a pre-trained Roberta model finetuned on the NLI dataset.
A topic model is a type of statistical model for discovering the abstract “topics” that occur in a collection of documents. Topic modeling is a frequently used text-mining tool for the discovery of hidden semantic structures in a text body. It is an unsupervised technique to know which “topic” a text document belongs to. See how to do topic modeling using Roberta and transformers. We will use a pre-trained Roberta model finetuned on the NLI dataset for getting embeddings and then do topic modelling.
Identifying Time-varying Drivers for Social Media Issues and Conversations | Chapter 5 | Recent Studies in Mathematics and Computer Science Vol. 1
Successfully understanding of social media conversation growth, dissemination and extinction is a challenging task that relies on identifying groups, group influence, diffusion models, forecast models, social dynamics and text analytics. In this problem, we concentrate on the description of a novel approach for identifying drivers of the direction and momentum of social conversations, including the spread of mood, sentiment and issues. The approach first groups potential drivers of conversation based on variability. The primary driver in each group is then selected. Finally, the relationship between the selected drivers and the topic outcome is calculated and displayed visually. This enables the quick identification of the form and structure of the conversation and allows us to predict momentum, direction, contagion risks, potential responses and interventions. There is a huge amount of data in the form of text available today in the internet across various channels – social media, news articles, blogs, e-commerce websites. Most of this data is a part of some “conversation” or the other where real-world entities discuss, analyze, comment, exchange information in the form of written expressions in textual format. Driver Modeling on textual data can be useful in observing the key drivers which are driving the “conversation” coupled with the associated sentiments and mood states for the observed key drivers. These insights about the key conversational drivers are often used in a variety of domains such as tracking news cycles, stock movements, legislation developments, brand image, viral breakouts and much more. Author(s) Details Dr. Gurpreet Singh Bawa Data Scientist Senior Principal, Accenture, India. View Book - http://bp.bookpi.org/index.php/bpi/catalog/book/153
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AaaaaaH! Such a neat project! I love seeing domestic-things and digital humanities work intersect!
Check out the topic modeling, the mapping and explore the wonderful collection of cookbooks pulled together through metadata work!
"lda2vec: Mixing Dirichlet Topic Models and Word Embeddings" by @chrisemoody https://t.co/AbRDRbOQdS ht @samim #nlp https://t.co/U9yejytX5l
