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Sentiment Analysis Based Requirement Evolution Prediction

College of Computer and Information Science, Chongqing Normal University, Chongqing 401331, China
Institute of Education Informatization, College of Teacher Education, Wenzhou University, Wenzhou 325035, China
Author to whom correspondence should be addressed.
Future Internet 2019, 11(2), 52;
Received: 12 January 2019 / Revised: 2 February 2019 / Accepted: 11 February 2019 / Published: 21 February 2019
(This article belongs to the Special Issue Future Intelligent Systems and Networks 2019)
PDF [796 KB, uploaded 21 February 2019]


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. View Full-Text
Keywords: features prediction; sentiment analysis; LSTM features prediction; sentiment analysis; LSTM

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Zhao, L.; Zhao, A. Sentiment Analysis Based Requirement Evolution Prediction. Future Internet 2019, 11, 52.

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