IJEEC | Low Resource Domain Subjective Context Feature Extraction via Thematic Meta-learning

Indexing and Abstracting

Low Resource Domain Subjective Context Feature Extraction via Thematic Meta-learning

Vishesh Agarwal , Anil Goplani , Mohit Kumar Barai , Arindam Sarkar , Subhasis Sanyal

International Journal of Electrical, Electronics and Computers (IJECC), Vol-8,Issue-4, July - August 2023, Pages 1-6, 10.22161/eec.84.1

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Article Info: Received: 28 Jun Sep 2023; Accepted:25 Jul 2023; Date of Publication: 02 Aug 2023

Abstract:

The volume of the data is directly proportional to the model's accuracy in data analytics for any particular domain. Once a developing field or discipline becomes apparent, the scarcity of the data volume becomes a challenging proponent for the correctness of a model and prediction. In the proposed state-of-the-art, a transitive empirical method has been used within the same contextual domain to extract features from a low-resource part via a heterogeneous field with factual data. Even though an example of text processing has been used for brevity, it is not limited. The success rate of the proposed model is 78.37%, considering model performance. But when considering human subject matter experts, the accuracy is 81.2%.

Keywords:

Data Analytics, Feature Extraction, Feedback review, Natural Language Processing, Text Processing.

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