Sentiment Analysis Based Requirement Evolution Prediction

Lingling Zhao and Anping Zhao
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Lingling Zhao: College of Computer and Information Science, Chongqing Normal University, Chongqing 401331, China
Anping Zhao: Institute of Education Informatization, College of Teacher Education, Wenzhou University, Wenzhou 325035, China

Future Internet, 2019, vol. 11, issue 2, 1-14

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.

Keywords: features prediction; sentiment analysis; LSTM (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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