Computational treatment of natural language text for intent detection

Adekotujo A.S., Enikuomehin T., Aribisala B., Mazzara M., Zubair A.F.
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Intent detection plays a crucial role in task-oriented conversational systems. To understand the user’s goal, the system relies on its intent detector to classify the user’s utterance, which may be expressed in different forms of natural language, into intent classes. However, lack of data, and the efficacy of intent detection systems has been hindered by the fact that the user’s intent text is typically characterized by short, general sentences and colloquial expressions. The process of algorithmically determining user intent from a given statement is known as intent detection. The goal of this study is to develop an intent detection model that will accurately classify and detect user intent. The model calculates the similarity score of the three models used to determine their similarities. The proposed model uses Contextual Semantic Search (CSS) capabilities for semantic search, Latent Dirichlet Allocation (LDA) for topic modeling, the Bidirectional Encoder Representations from Transformers (BERT) semantic matching technique, and the combination of LDA and BERT for text classification and detection. The dataset acquired is from the broad twitter corpus (BTC) and comprises various meta data. To prepare the data for analysis, a pre-processing step was applied. A sample of 1432 instances were selected out of the 5000 available datasets because manual annotation is required and could be time-consuming. To compare the performance of the model with the existing model, the similarity scores, precision, recall, f1 score, and accuracy were computed. The results revealed that LDA-BERT achieved an accuracy of 95.88% for intent detection, BERT with an accuracy of 93.84%, and LDA with an accuracy of 92.23%. This shows that LDA-BERT performs better than other models. It is hoped that the novel model will aid in ensuring information security and social media intelligence. For future work, an unsupervised LDA-BERT without any labeled data can be studied with the model.

Keywords: hate speech, intent classification, Twitter posts, sentiment analysis, opinion mining, intent identification from Twitter posts
Citation in English: Adekotujo A.S., Enikuomehin T., Aribisala B., Mazzara M., Zubair A.F. Computational treatment of natural language text for intent detection // Computer Research and Modeling, 2024, vol. 16, no. 7, pp. 1539-1554
Citation in English: Adekotujo A.S., Enikuomehin T., Aribisala B., Mazzara M., Zubair A.F. Computational treatment of natural language text for intent detection // Computer Research and Modeling, 2024, vol. 16, no. 7, pp. 1539-1554
DOI: 10.20537/2076-7633-2024-16-7-1539-1554
URL: http://crm-en.ics.org.ru/journal/article/3552/
Creative Commons License This work is licensed under a Creative Commons Attribution-NoDerivs 3.0 Unported License.

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