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Topic labeling is an (NLP) Natural Language Processing technique that enables you to automate tagging and organizing massive values of textual data based on the topic or theme.

On a daily basis, businesses generate a large volume of documents and unstructured text such as emails, social media posts, reviews, forum discussions, and customer support tickets. But when it comes to analyzing and making sense of this data, it is far too big to process manually. Even if you go ahead and start analyzing the data manually, it will be too time-consuming and you are bound to make mistakes.

This is where topic labeling can help you out. It can make it much easier and faster to accurately analyze and extract large volumes of data. In this blog, we will discuss what topic labeling is and how you can use it to analyze unstructured text.

What is Topic Labeling?

Topic labeling also known as topic extraction is a machine learning and NLP technique that examines and organizes large volumes of unstructured text data. It can tag and categorize documents or even each paragraph based on the topic or theme of the text.

Topic Labelling Approaches

Topic Modelling: It is an unsupervised machine learning technique. It can infer patterns and cluster comparable utterances without the need for subject tags or training data in advance. However, there is a drawback to this type of algorithm: it lacks accuracy.

Topic Extraction/Labeling: This approach requires knowing the topics before it starts analyzing. You need to tag a substantial volume of data to train the classifier. While the approach is more time-consuming than topic modeling, in the long run, it is way more accurate. It all depends on the quality of the data you train it with.

Levels at which you can apply topic labeling:

Sub-sentence level: Sub-expression topics are derived from the context of a given sentence using a topic model. A product review, for example, could include multiple themes in a single line.

Sentence Level: The subject of a single sentence can be derived using the topic model. The headline of a news story, for example.

Document Level: A text’s various themes can be gleaned using a topic model. Consider the subject matter of an email, a news story, or a blog.

When Should You Use Topic Labeling?

topic extraction

Internal documents, customer interactions, and the web all contain enormous volumes of content that may be evaluated quickly and effectively with subject tagging. Yes, you could do it manually, but it would be a time-consuming and costly operation that would be far from precise.

Organizations can utilize BytesView’s topic labeling, one of its text analysis techniques to boost team efficiency, automate business procedures, gain insight from data, and save hours of manual processing time.

Consider that if you want to learn what others are saying about your product, you’ll need to read through a large volume of reviews. If you want to know which of your product’s features (themes) are being discussed the most, you may use topic labeling in conjunction with sentiment analysis to determine how people feel about them. This method is known as aspect-based sentiment analysis.

Monitoring social media, customer service, and the voice of the customer (VoC) are just a few examples of potential subject analysis applications. It can also be utilized for other things like business intelligence and sales and marketing.

The Importance of Topic Labeling

Every day, organizations generate and accumulate massive amounts of unstructured text data. Automated topic labeling approaches provide a boatload of benefits including supporting firms in making better decisions, streamlining operations, finding new patterns, and identifying trends.

When it comes to sorting through all this data, machine learning models are critical. We can rapidly and efficiently scan large amounts of text using topic identification to see what our clients are talking about. Let’s look at some of its many benefits.

Scalable Data Analysis

Automated topic discovery would be far more efficient and cost-effective than searching through a vast database manually. The automation of subject analysis via machine learning allows for endless data scanning, offering up new avenues for getting meaningful insights.

Analysis in Real-time

By combining topic tagging with additional natural language processing techniques, such as sentiment analysis, you can gain a real-time picture of what your consumers are saying about your product. Finally, you may utilize this data to make data-driven decisions 24 hours a day, seven days a week.

Consistent Analysis Standards

Natural language processing (NLP), which combines statistics, computational linguistics, and computer science, is the foundation of automated topic analysis.

Applications of Topic Labeling

Now that we know what topic labeling is, let’s look at some of its applications.

Social Media Monitoring

Social media is a vast pool of user-generated often related to brands, organizations, products, and services. For example, users on Twitter alone send 500 million tweets every day.

With these immense volumes of textual data even finding the information related to a relevant topic is difficult, let alone analyze it. Although, following conversations related to your brand, and its products or services

Imagine you work for a fast-food corporation; you can use topic labeling to check what users are talking about your brand or any of its products.

Brand Monitoring

If you are talking about brand monitoring, just tracking social media conversations is not enough. You will have to track blogs, news articles, reviews, consumer forums, and more to track everything that is said or talked about your brand. Topic labeling can help you extract and categorize text data related to your brand from all over the web.

It makes it easier for you to track crucial elements that can tarnish your brand or further raise it in the eyes of the consumers. For example, user reviews play a crucial role in increasing sales. I mean, what would you trust a consumer, an advert, or genuine feedback from an existing customer. If customers post negative feedback, it will definitively decrease your sales and vice versa. With topic labeling, you can easily analyze reviews and act upon the negative ones which give you a chance to retain the customer.

Furthermore, it will also help you identify key issues, complaints, and grievances that your customers have and resolve them to provide a better user experience.

Product Analytics

Making improvements to an existing product or developing from scratch is not easy. You need insights about what the consumers’ needs are, which features are most sought-after, and which combination of features works the most. To get these insights you can dive into the data of your competitors. You can analyze their products and services, how customers respond to them, which improvements do they seek, and which features are unnecessary.

With topic labeling, you can easily gather this data and analyze it to design and develop an effective product or service for your target audience.

Closing Thoughts 

Topic labeling allows for the rapid and simple detection of topics and subjects within large collections of text data. Topic extraction enables you to automate your company processes ranging from customer service to social media monitoring and beyond. It allows you to do complex activities more efficiently and gain useful insights from your data that will lead to better business decisions. Start training your custom topic labeling model today with BytesView.

Topic Labeling: A Definitive Guide
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Topic Labeling: A Definitive Guide
Topic Labeling: A Definitive Guide
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