NLP Crowdsource-Based Innovation Recommendation System

Abstract

Numerous contributors share social problem ideas through open innovation. However, manually analyzing and processing many ideas, extracting information, and identifying expert ideas is difficult. Natural language processing can analyze and process crowd ideas. Our solution outsources creative tasks to crowds and automatically processes ideas. After processing unstructured texts, our solution analyzes text dependencies to extract syntactic sets. The third step, named entity recognition, detects proper names, organization names, locations, countries, etc. in texts. Key term extraction summarizes a text's meaning in the most useful key terms. Next, we find the most important sentences in texts to summarize ideas. Our sixth step extracts text patterns using the previous steps' results. The final step is identifying expert idea sources based on crowd idea patterns. We prove our method works in two studies. First, we compared named entity extraction and syntactic sets to human judgment. Second, we assessed key term extraction to a natural language processing API. Finally, we asked an economics expert to identify expert ideas in web form responses and compared our results with an online API this contributor used. Our method for textual data exploration produces promising results.