Biography

In natural language processing (NLP), language model is doubtlessly an intrinsic element, as it plays a fundamental role in many conventional NLP tasks, e.g., speech recognition to image captioning etc. Therefore, learning an exceptional language model usually enhance the hidden aspects or metrics; forging its pivotal role in NLP. Language models are gaining popularity as of the abundance of online texts, comments and reviews. Due to the advancement of e-commerce, people do write their reviews about the products they have received. In crowdfunding sites, comments are so critical that negative reviews can damage the reputation of the product’s creator or can affect the buying of others. Life is too fast these days that people find it difficult to go through abundant of text data to take a decision. Therefore, topic discovery is quite valuable in various aspects as of saving time of the user, providing the summary of text in form of discussion topics, and providing contextual information etc. Topic models are being studied for decades and are of fundamental importance as these models act as a tool in order to infer the latent topics and extracting semantic structure of a document. In this speech, we have used the Latent Topic Model (LDA) in order to generate topics for crowdfunding comments. Our proposed model is recurrent neural network (RNN) based language model, which uses the latent topics generated by LDA, is constructed to extract the comprehensive semantic meaning related words in comments. Moreover, this combined approach is of better capability on creating topic clusters then traditional ones, which signifies that blending the information from deep learning and topic modeling is a substantial way to generate an improved understanding of crowdfunding comments.