## 7 Discussions and Concluding Remarks
Third, there is plenty of data on the internet, which is beneficial for almost every organization, beating informational uncertainty and economic risks remains a hefty challenge for people. There is an ample amount of work done on user-generated content, such as comments and reviews, for different domain-specific applications. However, in crowdfunding, these comments and reviews are often ignored. Due to an increasing threat of fraudulent and suspicious actions on such forums, accurate and timely analysis of the content generated by the stakeholders can help us mitigate these risks. Therefore, we are finding a project's credibility based on both the content and behavioral patterns of the creator. Fourth, the optimized recommendations based on the project's credibility is also a significant addition in the crowdfunding domain. Moreover, this module gives weight to the user preferences and helps them invest in the most credible project. In Table 8, a comparison of recent works in crowdfunding with our proposed approach is presented. Here, we have compared our approach and interests with few past recent papers (since 2015) in \begin{table}
\begin{tabular}{c c c} \hline
**Batch No.** & **Period** & **Accuracy** \\ \hline Batch 1 & Launched until Fundraising period & 20\% \\ Batch 2 & After Fundraising period & 55\% \\ Batch 3 & Before expected delivery & 67\% \\ Batch 4 & After Expected Delivery & 83\% \\ Batch 5 & Top 60 most recent comments & 88\% \\ \hline \end{tabular}
\end{table} 
Table 7: Project classification accuracy based on different comment batch sizes. crowdfunding. In [51] and [52], different crowdfunding domains are targeted, such as tourism and medical crowdfunding, respectively. Though both of these studies have attempted risk assessments and fraud identification, none of these studies focus on the project content or linguistic features. In [53; 54; 55; 56; 57], Kickstarter is used as a crowdfunding platform primarily for project success predictions. The work in [58; 59] and [26], targets to acquire knowledge of fraudulent behaviors in crowdfunding by analyzing linguistic patterns. In [60], comments and updates of successful projects are used to estimate the delivery duration of rewards. In comparison with all the recent works, and to the best of our knowledge, the proposed approach is first of its kind in considering investors' comments and utilizing them for optimized and reliable recommendations. The idea of using user reviews and comments for prediction and recommendation is not novel. Many studies have been conducted, which make use of reviews to tackle different problems in diverse fields. Some studies have focused on the reviews content to find the satisfaction of hotel guests [61], tourist satisfaction [62], and sentiments related to a product [63]. Therefore, we compared our approach with other recent approaches in Table 9. All of the studies [61; 62; 63; 64; 65; 66; 67; 68] are very recent and are targeting different problem domains by using online reviews. All the studies either detect sentiments or the credibility of a review. Our proposed approach is based on a deep contextual understanding of the comments for discovering discussion patterns and making recommendations accordingly. However, there are some limitations to our work that can be overcome in future research. First, we have not taken all the profile related features of project creators into consideration. For example, the history of their work on Kickstarter in terms of the number of projects they have been part of (either as a creator or an investor) can tell a lot about their credibility. Second, it is very challenging to evaluate the risk for each user. There might be some investors who have a great interest in a specific project, and they are ready to take any monetary risk to try that project regardless of the credibility of a project. In that case, a project with an adverse credibility level will less likely be suggested.