Dataset For Intent Classification
What is the Intent Classification Dataset?
The intent classification dataset is used for classifying the intention of a text/ sentence. In the intent classification dataset, every text/sentence of the dataset is associated with one or more corresponding intent labels.
Details About intent classification dataset:-
- Input type :- Text
- Dataset type :- Multi-Label
- Size of the dataset :- 12k
- Number of intent labels :- 11
- Intent Labels :- Complain, Criticize, Direct, Praise, Quit, Sell, Compare, Inquiry, Purchase, Wish, Other.
This intent Classification Dataset has different kinds of sentences such as questions, suggestions, feedback, etc. about different products.
How Can the Intent Classification Dataset Be Used?
By using an intent classification dataset, you can implement an intent classification model. By using this model, you can identify customer’s intentions about your company/organization purchasing products or their problems with your products/services.
Where Do We Collect Data To Create the Intent Classification Dataset?
We extract customer reviews or feedback from different social media platforms such as tweets from Twitter, Facebook messages, reviews from Amazon, and Flipkart. Nowadays, company customers express their feelings on corresponding company social media accounts such as Twitter, Facebook, etc. That feedback maybe
- advice to the company towards product/service improvement
- comparison between your company products and your competitor’s products
- how they feel after using your company’s products, etc.
We are extracting nearly 12K reviews that have different intentions of different customers’ feedbacks towards the company’s products/services.
What Kind of Data Annotation is Performed on Data To Prepare the Intent Classification Dataset?
Our extracted data have different kinds of sentences, such as queries, advice, positive/negative feedback, disappointments about products, etc. We applied a few text preprocessing techniques on extracted data to making raw data as more quality data and sent it to the annotator’s team for annotation purposes.
Annotators read every sentence and tag one or more available corresponding intent labels to every sentence using our annotation tool. This process of annotation is called document/sentence classification annotation process. Here annotators tag labels for the whole sentence.If every single data point in the dataset has one or more labels that dataset is multi-labeled.
- For example, if a customer gives feedback like “I am very happy with these products but unsatisfied with the product delivery speed, I hope I will get the product fastly.”. Here customer intention about the product is Praise and Wish.
After completing the annotation process by the annotator’s team on the whole data, annotated data sent to the verification team for checking, are annotators tag labels correctly or not?. Which the annotators incorrectly label sentences, all are sent back to the annotators’ team for the second round labeling process. This process is repetitive until the whole dataset has correct labels.
What Are the Problems Solving or Benefits of Using the Intent Classification Dataset?
- If you are building intent classification using this intent dataset, that model allows you to focus on more customer-centric types such as customer support and sales area. Intent classification is a crucial point for your business to provide more personalized products to customers and respond to customer queries based on their intent.
- Intent classifiers can pinpoint important customers who express their interest in products that queries directly send to the sales team.
- If your organization conducts campaigns, we can receive more customer interactions/queries. In this case, you can apply an intent classifier dataset to build a model and use that model on a bunch of questions to find purchase intent. You can contact those who are having the intention of purchasing products immediately to improve your sales.
- You can also generate reports on your campaign, such as how many customers are interested in purchasing your product? How many members have positive/ impressions on your company’s products? Etc.
- Auto dealers receive too many calls regarding sales inquiries, service requests, vendor questions, job opportunities, etc. Understanding the intention behind every call can give dealers a positive experience while doing customer engagement that will help organizations boost their sales and revenue.
- You can’t capture all intents of different users by reading thousands of feedbacks. By using the intent classification, you can save more time and an employer’s effort.
Which Business Applications Can Use the Intent Classification Dataset?
Every company needs to know every customer’s intent behind their action or feedback to experience more benefits towards their business growth. Because companies will be able to better understand their customers’ feedback on their products or services. Intent classification is the new evaluation strategy for companies to evaluate customer’s feedbacks.
Role of Intent Classification Dataset in Chatbot
This intent classification dataset is mostly used in AI-Conversational Chatbots to analyze the intention behind the customer information.
Natural Language Processing (NLP) empowers chatbots to understand the client’s demands. However, the conversational engine unit in NLP is critical in making the chatbot more relevant and offering customized discussion encounters to customers. The significant part of this chatbot conversation is intent classification. The Chatbot model aims to provide responses based on the customer message/query’s intention. So while building AI-Conversational Chatbot, the dataset of intent classification plays an important role to get knowledge about customers’ intentions/feelings. Chatbot or dialogue systems need to handle a higher number of intents. In our intent classification dataset have 11 intent labels. So we can identify the most important intents.
Nowadays, customers contact organizations for their problems or any query from dialogue boxes. If you want to help your customers, you need to identify the query’s intent or anything else. Chatbot or dialogue systems will identify the intention of the query and give corresponding responses to their customers. If you want to automate the interaction through chatbots, we should develop that model using the intent classification dataset.
Due to the importance of intention classification in Chatbot or Dialogue systems, we need to use the proper intent classification dataset. Our intent classification dataset is suitable for this kind of system because it has the most important intents about products and is labeled correctly.
Role in the Recommendation System
Another technology that mostly uses intent classification mechanisms to provide more accurate results to organizations and customers is the recommendation system. Recommendation systems act as filtering the customers based on their activities and intentions and providing suitable products. Solid suggestions can result in more powerful customized substance and publicizing, hence expanding the lead customer conversation rate.
In the recommendation system, systems/models need to identify customer intentions based on their actions, such as purchasing or liked products, to recommend the most beneficial products or services to users. This intent classification dataset plays a vital role because if your dataset does not have an efficient amount of data and number of intents, you can not identify all the customer’s indentation.