Topic Predictions and Optimized Recommendation Mechanism Based on Integrated Topic Modeling and Deep Neural Networks in Crowdfunding Platforms

Abstract

The accelerated growth rate of internet users and its applications, primarily e-business, has accustomed people to write their comments and reviews about the product they received. These reviews are remarkably competent to shape customers’ decisions. However, in crowdfunding, where investors finance innovative ideas in exchange for some rewards or products, the comments of investors are often ignored. These comments can play a markedly significant role in helping crowdfunding platforms to battle against the bitter challenge of fraudulent activities. We take advantage of the language modeling techniques and aim to merge them with neural networks to identify some hidden discussion patterns in the comments. Our objective is to design a language modeling based neural network architecture, where Recurrent Neural Networks (RNN) Long Short-Term Memory (LSTM) is used to predict discussion trends, i.e., either towards scam or non-scam. LSTM layers are fed with latent topic distribution learned from the pre-trained Latent Dirichlet Allocation (LDA) model. In order to optimize the recommendations, we used Particle Swarm Optimization (PSO) as a baseline algorithm. This module helps investors find secure projects to invest in (with the highest chances of delivery) within their preferred categories. We used prediction accuracy, an optimal number of identified topics, and the number of epochs, as metrics of performance evaluation for the proposed approach. We compared our results with simple Neural Networks (NNs) and NN-LDA based on these performance metrics. The strengths of both integrated models suggest that the proposed model can play a substantial role in a better understanding of crowdfunding comments.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 23 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.