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.
Input type :- Text
Dataset type:- Multi-Label
Size of the dataset:- 12k
Number of intent labels:- 11
Intent Labels:- Complaint, 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.
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.
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
We are extracting nearly 12K reviews that have different intentions of different customers’ feedbacks towards the company’s products/services.
Our extracted data have different kinds of sentences, such as queries, advice, positive/negative feedback, disappointments about products, etc. We applied a few texts preprocessing techniques on extracted data to making raw data 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 the 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.
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.
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.
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. A 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.
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.