Next Article in Journal
Embedded Deep Learning for Ship Detection and Recognition
Next Article in Special Issue
Open Data for Open Innovation: An Analysis of Literature Characteristics
Previous Article in Journal
A Fusion Load Disaggregation Method Based on Clustering Algorithm and Support Vector Regression Optimization for Low Sampling Data
Previous Article in Special Issue
Audio-Visual Genres and Polymediation in Successful Spanish YouTubers
Article Menu
Issue 2 (February) cover image

Export Article

Open AccessArticle

Sentiment Analysis Based Requirement Evolution Prediction

1
College of Computer and Information Science, Chongqing Normal University, Chongqing 401331, China
2
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; https://doi.org/10.3390/fi11020052
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]
  |  

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

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

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

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Future Internet EISSN 1999-5903 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top