Abstract
To facilitate product developers capturing the varying requirements from users to support their feature evolution process, requirements evolution prediction from massive review texts is in fact of great importance. The proposed framework combines a supervised deep learning neural network with an unsupervised hierarchical topic model to analyze user reviews automatically for product feature requirements evolution prediction. The approach is to discover hierarchical product feature requirements from the hierarchical topic model and to identify their sentiment by the Long Short-term Memory (LSTM) with word embedding, which not only models hierarchical product requirement features from general to specific, but also identifies sentiment orientation to better correspond to the different hierarchies of product features. The evaluation and experimental results show that the proposed approach is effective and feasible.
Highlights
The product feature requirements are the descriptions of the features and functionalities of the target product
We demonstrate the hypothesis for the task of hierarchical product features requirement evolution prediction by sentiment analysis with a recurrent neural network and the Hierarchical Latent Dirichlet Allocation (HLDA)
In order to model feature granularity and discover different requirements of users based on sentiment, we present a hierarchical feature requirement prediction method based on Recurrent Neural Network (RNN) and HLDA to solve the problem of hierarchical feature-dependent sentiment discovery
Summary
The product feature requirements are the descriptions of the features and functionalities of the target product This provides a way for a product developer to get and understand the expectations of users of the product. Product feature systems are undergoing continuing changes and a continuous updating process to satisfy users’ requirements. In a real application, we need to identify the feature requirements of given products and the opinions being expressed towards each feature at different granularities from users. Analysis of specific product features and sentiments at a single granularity cannot capture the requirements of all users. It is necessary to convey the specific features and sentiments at different granularities of products to product developers so that they can understand the different levels of user concerns. It is more challenging to model feature granularity and to incorporate sentiment
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