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Software . 2019
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Software . 2019
License: CC BY
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Software . 2019
License: CC BY
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Automatic Analysis of User Reviews to Predict User Ratings of Open Source Software

integration_instructionsResearch softwarekeyboard_double_arrow_right Software 24 May 2019 bzj Publisher:Zenodo
Authors: Gezici, Bahar; Necva Bölücü; Tarhan, Ayça; Can, Burcu;

doi: 10.5281/zenodo.3228124 , 10.5281/zenodo.3228123

Automatic Analysis of User Reviews to Predict User Ratings of Open Source Software

Abstract

"Automatic Analysis of User Reviews to Predict User Ratings of Open Source Software " Requirements : Python 3.5 Tensorflow Required files: GloVe 42B 300d Amazon Review To extract dataset: python extract_dataset.py To run LSTM model: python main.py

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Keywords

Open Source Software, OSS, Transfer Learning, Sentiment Analysis, Deep Learning, User Satisfaction, Software Quality, Open Source Software, OSS, Transfer Learning, Sentiment Analysis, Deep Learning, User Satisfaction, Software Quality

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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