A Novel Hybrid Approach for Intent Creation and Detection Using K-Means-Based Topic Clustering and Heuristic-Based Capsule Network

Abstract

Background: The social media revolution has offered new facilities and opportunities to the online community to communicate their intentions, opinions, and views regarding products, services, policies, and events. The identification of intent focuses on the detection of intents from user reviews, that is, whether the specific review of the user includes intention or not. Intent mining is also named intent identification which helps business organizations to identify the purchase intentions of users. However, detecting user intentions encoded in text queries is a complicated task in several Natural Language Processing (NLP) applications such as robots, smart agents, personal assistants, and search engines. The existing research works have discovered the utilization of several machine learning techniques to detect the intents from queries of users. Most works consider intent detection as a classification problem, with utterances as predefined intents. Research question: Whether the researcher resolves the detection of user intentions encoded in text queries? How the researcher solves the existing challenges based on intent mining? Purpose: The main contribution of the research is to design and implement intent detection using topic clustering and deep learning. Methodology: Initially, the dataset related to diverse queries is gathered. Then, the label creation is performed by clustering. The clustering is performed by a k-means clustering model with a cosine similarity function. Once the clustering is performed for different queries, the label is created, which is used to train the network under the detection process. For the detection, this paper uses a Heuristic-based Capsule Network (H-CapNet) that could perform the intention for a new query. The hybrid meta-heuristic algorithm with Escaping Energy searched Grey–Harris Hawks Algorithm (EEG-HHA) is used for improving the capsule network. Validation: Experimental analysis shows that the developed method has superior performance in evaluating standard datasets with other approaches. Results: From the simulation results, the accuracy of the developed EEG-HHA-CapNet for dataset 1 is secured at 3%, 1.6%, 2%, and 1.1% increased than PSO-CapNet, WOA-CapNet, HHO-CapNet, and GWO-CapNet. Conclusion: Thus, the designed user intent detection models reveal their more advanced performance based on the diverse performance and error metrics for datasets 1 and 2.