Topic labeling is a machine learning technique for organizing and understanding massive amounts of text data by assigning “tags” or groups based on the topic or theme of each individual paragraph.
Topic labeling is a technique for unsupervised machine learning. This implies it can infer patterns and group similar statements without the requirement to establish subject tags or train data.
Consider the situation when you need to examine a big number of reviews to determine what people are saying about your product. Topic labeling and sentiment analysis could be combined to establish which features or subjects of your product are being discussed most frequently and how people feel about them. Aspect-based sentiment analysis is the name for this method.
Topic labeling can be used for a variety of purposes, including social media monitoring, customer support, the voice of customer analysis, business analysis, brand management, SEO, product analytics, and organizational learning, in addition to brand monitoring.
Topic labeling is applied at different levels of scope:
- At the document level, the topic model extracts the various subjects from a complete text. Consider the subject of an email or a news story.
- The topic model obtains the topic of a single sentence at the sentence level. Consider the subject of a news article’s headline.
- The topic model retrieves the topic of sub-expressions from within a phrase at the sub-sentence level. For example, within a single sentence of a product evaluation, numerous subjects can be discussed.
Application of Topic Labeling
- Detect and track the different aspects of your business that people are discussing the most to gain insights about your brand using topic identification and analysis.
- When you receive client contacts, you may utilize topic classifiers to identify potential buyers and reach out to them straight immediately.
- Machines work faster than people and do not get tired, therefore they will never miss a sale, even as workloads increase.
- The higher the chances of closing purchase, the faster teams can notice and respond to buy intents through subject identification.
- The uniformity of the criteria ensures that all customer conversations are analyzed under the same settings, with the same processes and methods.
Tools to Use for Topic Labeling
BytesView is an efficient tool that you can use to automate the classification of documents with topic labeling and text categorization.
Large organizations in any industry have to process a ton of documents on a daily basis. It can help you segregate documents by identifying clusters of words from unstructured text data within minutes with guaranteed accuracy.
This tool is best used in conjunction with your social media channels because it can tell you exactly how people perceive your company’s social media accounts.
Another excellent tool for topic labeling is Talkwalker. It claims to have the best text analysis technology available, allowing it to distinguish sarcasm and other ambiguous forms of negative mentions.
Clarabridge is a multifaceted platform that includes customer experience management. Text analysis is a component of this solution. The text analysis in the tool is extremely detailed, taking into account parsing, framework, industry, and source.
MonkeyLearn is a text analysis program known for its adaptability. Simply create tags and then manually highlight different parts of the text to show which content belongs to which tag.
Over time, the software learns on its own and can process multiple files at the same time It contains a collection of pre-trained models for tasks such as topic labeling, sentiment analysis, keyword extraction, intent detection, and much more
Brandwatch is also one of my go-to analytics tools. It analyses brand sentiment, displays trends, and includes a cool feature called “image insights.” In the same way that topics can be linked with your brand’s name, the feature recognizes images associated with your brand’s logo.
Lexalytics is a business intelligence solution that analyses various types of text. Lexalytics works with social media comments, surveys, reviews, and any other type of text document. In addition to sentiment analysis, the tool performs emotion detection, theme extraction, and topic labeling, which can help users see the full context.