Probabilistic topic models are algorithms that aim to discover and annotate large collections of documents with thematic information without any prior annotations. They work by analyzing the statistical co-occurrence of words to identify topics, where a topic is a probability distribution over words. Documents are represented as mixtures of topics. For example, a document may have a 60% probability of being about biology, 30% about physics, and 10% about mathematics. Topics emerge from the statistical analysis and provide interpretable groups of correlated terms.

1. Topic Analysis in ARCOMEM
Yahoo Research Barcelona

2. What is Probabilistic Topic Modelling?
Exploring and retrieving meaningful information from large
collections of textual documents is a challenging task
Probabilistic topic models are a suite of algorithms (a framework)
that aim to discover and annotate large archives of documents
with thematic information.
They do not require any prior annotations or labeling of the
documents.
Topics emerge from the statistical analysis of the original texts

3. Probabilistic Topic Model
Topic models are based upon the idea that documents are mixtures
of topics, where a topic is a probability distribution over a fixed
vocabulary.
A topic model is a generative model for documents: it specifies a
simple probabilistic procedure by which documents can be generated.
The idea is to study the co-occurrence of words, assuming that
words that tend to co-occur frequently, express, or belong to, the
same semantic concept.
Example: A document (d) can be represented by the following mixture
of topics Biology Physics Mathematics
0,6 0,3 0,1
In the topic “Biology” words such as “Dna, genetic, evolution” have high
probability

4. Intuition behind topic modelling
Documents exhibit multiple topics
Each topic is individually interpretable, providing a probability
distribution over words that picks out a coherent cluster of
correlated terms
Evolution Biology
Genetics
Statistical
Analysis

5. The challenge is to identify, for each campaign, significant and
important topics that are relevant to the two user cases, broadcasting
and parliament libraries.
Topic analysis provides semantic useful categories which allow end-
users to search and browse content archives.

6. Try out on SARA: Trending topics

7. Try out on SARA: Statistical Topic Models