Idea Mining From Online Reviews Using Transformation-Based Natural Language Processing Tasks

• Published in: 2022 International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) pp. 894 - 899
• Main Authors: Mahdi, Hussain Falih, Gupta, Lav Kumar, Choudhury, Tanupriya, Bansal, Nikunj
• Format: Conference Proceeding
• Language: English
• Published: IEEE 20.10.2022
• Subjects: BERT; Bit error rate; Data mining; Feedforward neural networks; Idea Mining; Machine Learning; Machine learning algorithms; Natural language processing; Neural Networks; Sentence Similarity; Technological innovation; Text Mining; Transformers
• ISSN: 2770-7962
• DOI: 10.1109/ISMSIT56059.2022.9932785

Product reviews from online communities are good sources to innovative ideas. Making use of this unstructured data from these reviews is a complicated process. The major task of identifying good ideas is ensuring the correct reviews are picked up for consideration. We propose a method of mining ideas by exploring online reviews data with a particular interest in discovering helpful suggestions in innovation. We use a classifier model to categorize whether the reviews from an online community provide a suggestion. The non-suggestive texts were discarded from the further analysis. The texts are then processed further by a sentence similarity comparison module that uses the Bidirectional Encoder Representations from Transformers (BERT) model. We use the sentence similarity techniques to filter common suggestions that are attractive to many reviewers. We describe how to use BERT based model for Natural Language Tasks. We conclude that mining reviews can help extract ideas from online communities. This is a generic implementation and can be adopted in any domain. We also suggest a human intervention to assess the context of the generated ideas. This method can help researchers and professionals discover ideas buried in unstructured online reviews.