###### 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. topic modeling; Latent Dirichlet Allocation (LDA); Recurrent Neural Networks (RNN); Long Short-Term Memory (LSTM); optimization; Particle Swarm Optimization (PSO); crowdfunding scams; Kicksarter; objective function +
Footnote †: journal: Medical  
According to the Internet Crime Report 2018 [3], there are billions of dollars recorded as fraud on the internet, as shown in Figure 1. Crowd-thieving is enabling fraudsters to illegally raise money in terms of misrepresenting their ideas, taking an advance fee, investments, non-payments, or non-delivery or personal data breach, as shown in Figure 2.  
reviews represents the user preferences and product characteristics. Therefore, latent feature vectors can be obtained comparatively in a more natural way. In previous studies, user reviews are analyzed primarily through topic modeling-based approaches such as Latent Dirichlet Allocation (LDA), which aims to discover hidden themes in textual documents [6]. However, topic modeling approaches fail to capture contextual information [7]. In comparison with topic modeling, deep learning approaches, such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNNs), can capture the contextual and temporal dependencies. Regardless of the strength and power of these deep learning methods, there are still some limitations in terms of the sliding window size. For example, if the window size is too small, it can cause CNN to fail in connecting words to sentences. Similarly, RNN finds it challenging when it comes to sentence size; in other words, if a review is too long, RNN's ability to extract contextual information will be limited. To alleviate the problem, we suggest the integration of deep learning approaches with language modeling techniques. Therefore, we propose a hybrid method that takes advantage of both the solution domains, to provide a deeper understanding of the comments. The deep learning methods can help reserve the context information, whereas topic modeling focuses on word relationships, which mollifies the chances of information loss. For the language modeling part, we use the most common topic modeling approach, i.e., LDA.