Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Mobile apps are one of the building blocks of the mobile digital economy. A differentiating feature of mobile apps to traditional enterprise software is online reviews, which are available on app marketplaces and represent a valuable source of consumer feedback on the app.  create a supervised topic modeling approach for app developers to use mobile reviews as useful sources of quality and customer feedback, thereby complementing traditional software testing. The approach is based on a constrained matrix factorization that leverages the relationship beten term frequency and a given response variable in addition to co-occurrences beten terms to recover topics that are both predictive of consumer sentiment and useful for understanding the underlying textual themes. The factorization can provide guidance on a single app's performance as ll as systematically compare different apps over time for benchmarking of features and consumer sentiment.  apply our approach using a dataset of over 81,000 mobile reviews over several years for the two of the most revied online travel agent apps from the iOS and Google Play marketplaces.
['Shawn Mankad', 'Shengli Hu', 'Anandasivam Gopal']
I am writing a research paper on a idea Single Stage Prediction with Online Reviews for Mobile App Development and Management
[{"from": "human", "value": "I am writing a research paper on a idea Single Stage Prediction with Online Reviews for Mobile App Development and Management"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Mobile apps are one of the building blocks of the mobile digital economy. A differentiating feature of mobile apps to traditional enterprise software is online reviews, which are available on app marketplaces and represent a valuable source of consumer feedback on the app.  create a supervised topic modeling approach for app developers to use mobile reviews as useful sources of quality and customer feedback, thereby complementing traditional software testing. The approach is based on a constrained matrix factorization that leverages the relationship beten term frequency and a given response variable in addition to co-occurrences beten terms to recover topics that are both predictive of consumer sentiment and useful for understanding the underlying textual themes. The factorization can provide guidance on a single app's performance as ll as systematically compare different apps over time for benchmarking of features and consumer sentiment.  apply our approach using a dataset of over 81,000 mobile reviews over several years for the two of the most revied online travel agent apps from the iOS and Google Play marketplaces."}]