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Topic Modeling: Extracting Topics from Text Data

With the help of tags or categories that are assigned in accordance with the topic or theme of each individual text, topic analysis—also known as topic detection, topic modeling, or topic extraction—organizes and understands large collections of text data.

We discuss any subject to elicit some underlying meaning from a conversation with someone in real life. In natural language processing, a subject is the same as a collection of words that are somehow related.

In a group of documents, a topic model automatically identifies topics. The next step is to determine which topics appear in recent papers using a trained model. If certain sections of a document are pertinent to certain themes, the model may also be used to determine this.

What is Topic Modelling?

By scanning a collection of documents for word and phrase patterns, topic modeling is an unsupervised machine learning technique that can automatically cluster word groups and concordant expressions that most accurately describe the set.

This kind of machine learning is referred to as unsupervised machine learning because it does not require a preexisting list of tags or training data that has already been classified by humans.

But these should not be confused with the various topic classification models, which are supervised machine learning methods. It’s important to understand the topic of a collection of writings before studying it. These themes are manually added to the data so that a topic classifier can learn from them and predict the future.

The process of pulling necessary attributes out of a word cloud is called topic modeling. This is important because each word in the corpus is considered a feature by NLP. In order to avoid wasting time sifting through all of the text in the data, feature reduction lets us concentrate on the important information.

1.     Assign Topic Labels to Chats

This means that for each document it is trained on, the LDA model provides topic weights. Now, switching to a supervised approach is simple: the highest weight vector component is chosen, and the associated topic is used as the target label for the given chat document.

In the steps that follow, only the texts with a dominant topic weight greater than 0.5 have been kept in order to increase the confidence of the assignment of the label (other thresholds have been tested too, but 0.5 was the value which has allowed to keep at the same time a reasonable proportion of online chats in the dataset.)

2.     Classify New Chats

The multinomial logistic regression model has been trained and tested to classify new chats to the corresponding topic labels after developing a setting compatible with a supervised machine learning algorithm.

Over the course of the 4-fold cross-validation technique’s iterations, the classification results in terms of precision and recall have consistently been above 0.96 (on average among the 15 topic classes).

There is, however, evidence that this strategy can extract pertinent and interesting topical information from a corpus of unstructured texts and offer an algorithm that correctly tags texts from previously unread online chats.

Wrapping Up

So, this was all you needed to know about topic modeling. Being a subsidiary of Sambodhi Research and Communications Pvt. Ltd., Education Nest is a global knowledge exchange platform that empowers learners with data-driven decision making skills.

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