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[1] Zhoujun Li,et al. Emerging topic detection for organizations from microblogs , 2013, SIGIR.
[2] Christos Faloutsos,et al. Why people hate your app: making sense of user feedback in a mobile app store , 2013, KDD.
[3] Ee-Peng Lim,et al. Finding Bursty Topics from Microblogs , 2012, ACL.
[4] Gabriele Bavota,et al. Pattern-Based Mining of Opinions in Q&A Websites , 2019, 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE).
[5] Koushik Sen,et al. When deep learning met code search , 2019, ESEC/SIGSOFT FSE.
[6] M. Narasimha Murty,et al. On Finding the Natural Number of Topics with Latent Dirichlet Allocation: Some Observations , 2010, PAKDD.
[7] Harald C. Gall,et al. What would users change in my app? summarizing app reviews for recommending software changes , 2016, SIGSOFT FSE.
[8] Mehmet A. Orgun,et al. A survey on real-time event detection from the Twitter data stream , 2018, J. Inf. Sci..
[9] Jieming Zhu,et al. PAID: Prioritizing app issues for developers by tracking user reviews over versions , 2015, 2015 IEEE 26th International Symposium on Software Reliability Engineering (ISSRE).
[10] Jeffrey Pennington,et al. GloVe: Global Vectors for Word Representation , 2014, EMNLP.
[11] F. Wilcoxon. Individual Comparisons by Ranking Methods , 1945 .
[12] Bing Liu,et al. Mining and summarizing customer reviews , 2004, KDD.
[13] ChengXiang Zhai,et al. Automatic labeling of multinomial topic models , 2007, KDD '07.
[14] Abdolreza Abhari,et al. Cluster-discovery of Twitter messages for event detection and trending , 2015, J. Comput. Sci..
[15] Jacob Cohen,et al. Weighted kappa: Nominal scale agreement provision for scaled disagreement or partial credit. , 1968 .
[16] Danielle S. McNamara,et al. Handbook of latent semantic analysis , 2007 .
[17] Jeffrey Dean,et al. Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.
[18] Jianxin Li,et al. Bursty event detection from microblog: a distributed and incremental approach , 2016, Concurr. Comput. Pract. Exp..
[19] Michael R. Lyu,et al. Online App Review Analysis for Identifying Emerging Issues , 2018, 2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE).
[20] Alain Abran,et al. A systematic literature review: Opinion mining studies from mobile app store user reviews , 2017, J. Syst. Softw..
[21] Walid Maalej,et al. How Do Users Like This Feature? A Fine Grained Sentiment Analysis of App Reviews , 2014, 2014 IEEE 22nd International Requirements Engineering Conference (RE).
[22] Harald C. Gall,et al. Recommending and Localizing Change Requests for Mobile Apps Based on User Reviews , 2017, 2017 IEEE/ACM 39th International Conference on Software Engineering (ICSE).
[23] Foutse Khomh,et al. Opiner: An opinion search and summarization engine for APIs , 2017, 2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE).
[24] Elisabeth Platzer,et al. Opportunities of automated motive-based user review analysis in the context of mobile app acceptance , 2011 .
[25] Jun'ichi Tsujii,et al. A Latent Concept Topic Model for Robust Topic Inference Using Word Embeddings , 2016, ACL.
[26] Minhaz Fahim Zibran,et al. Leveraging Automated Sentiment Analysis in Software Engineering , 2017, 2017 IEEE/ACM 14th International Conference on Mining Software Repositories (MSR).
[27] Mark Harman,et al. Causal impact analysis for app releases in google play , 2016, SIGSOFT FSE.
[28] Chunyan Miao,et al. Generative Topic Embedding: a Continuous Representation of Documents , 2016, ACL.
[29] Hui Xu,et al. AR-Tracker: Track the Dynamics of Mobile Apps via User Review Mining , 2015, 2015 IEEE Symposium on Service-Oriented System Engineering.
[30] Chenliang Li,et al. Twevent: segment-based event detection from tweets , 2012, CIKM.
[31] Nicole Novielli,et al. Sentiment Polarity Detection for Software Development , 2017, Empirical Software Engineering.
[32] Jakob Uszkoreit,et al. A Decomposable Attention Model for Natural Language Inference , 2016, EMNLP.
[33] S. Ejaz Ahmed. Effect Sizes for Research: A Broad Application Approach , 2006, Technometrics.
[34] Yuanyuan Zhang,et al. A Survey of App Store Analysis for Software Engineering , 2017, IEEE Transactions on Software Engineering.
[35] Xiaodong Gu,et al. "What Parts of Your Apps are Loved by Users?" (T) , 2015, 2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE).
[36] Michael S. Bernstein,et al. Twitinfo: aggregating and visualizing microblogs for event exploration , 2011, CHI.
[37] Peter J. Bentley,et al. Investigating app store ranking algorithms using a simulation of mobile app ecosystems , 2013, 2013 IEEE Congress on Evolutionary Computation.
[38] Aixin Sun,et al. Topic Modeling for Short Texts with Auxiliary Word Embeddings , 2016, SIGIR.
[39] Maleknaz Nayebi,et al. Release Practices for Mobile Apps -- What do Users and Developers Think? , 2016, 2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER).
[40] Tunga Güngör,et al. Part-of-Speech Tagging , 2005 .
[41] Harald C. Gall,et al. Exploring the integration of user feedback in automated testing of Android applications , 2018, 2018 IEEE 25th International Conference on Software Analysis, Evolution and Reengineering (SANER).
[42] Daniel Barbará,et al. On-line LDA: Adaptive Topic Models for Mining Text Streams with Applications to Topic Detection and Tracking , 2008, 2008 Eighth IEEE International Conference on Data Mining.
[43] Ahmed E. Hassan,et al. Fresh apps: an empirical study of frequently-updated mobile apps in the Google play store , 2015, Empirical Software Engineering.
[44] Tung Thanh Nguyen,et al. Phrase-based extraction of user opinions in mobile app reviews , 2016, 2016 31st IEEE/ACM International Conference on Automated Software Engineering (ASE).
[45] Ahmed E. Hassan,et al. A survey on the use of topic models when mining software repositories , 2015, Empirical Software Engineering.
[46] Francis R. Bach,et al. Online Learning for Latent Dirichlet Allocation , 2010, NIPS.
[47] VARUN CHANDOLA,et al. Anomaly detection: A survey , 2009, CSUR.
[48] Ning Chen,et al. AR-miner: mining informative reviews for developers from mobile app marketplace , 2014, ICSE.
[49] Tiago P. Peixoto,et al. A network approach to topic models , 2017, Science Advances.
[50] Charles A. Sutton,et al. Autoencoding Variational Inference For Topic Models , 2017, ICLR.
[51] Ahmed E. Hassan,et al. What Do Mobile App Users Complain About? , 2015, IEEE Software.
[52] Walid Maalej,et al. Bug report, feature request, or simply praise? On automatically classifying app reviews , 2015, 2015 IEEE 23rd International Requirements Engineering Conference (RE).
[53] Quoc V. Le,et al. Distributed Representations of Sentences and Documents , 2014, ICML.
[54] Kenneth Ward Church,et al. Word Association Norms, Mutual Information, and Lexicography , 1989, ACL.
[55] Xiaohui Yan,et al. A biterm topic model for short texts , 2013, WWW.
[56] Tung Thanh Nguyen,et al. Mining User Opinions in Mobile App Reviews: A Keyword-Based Approach (T) , 2015, 2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE).
[57] Maleknaz Nayebi,et al. Analysis of marketed versus not-marketed mobile app releases , 2016 .
[58] Vivek Kumar Rangarajan Sridhar,et al. Unsupervised Topic Modeling for Short Texts Using Distributed Representations of Words , 2015, VS@HLT-NAACL.
[59] Di Jiang,et al. Latent Topic Embedding , 2016, COLING.
[60] Michael R. Lyu,et al. Emerging App Issue Identification from User Feedback: Experience on WeChat , 2019, 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP).
[61] David M. Blei,et al. Probabilistic topic models , 2012, Commun. ACM.
[62] Michael I. Jordan,et al. Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..
[63] Xiaohui Yan,et al. A Probabilistic Model for Bursty Topic Discovery in Microblogs , 2015, AAAI.
[64] Xiuzhen Zhang,et al. A probabilistic method for emerging topic tracking in Microblog stream , 2016, World Wide Web.
[65] Jianhua Lin,et al. Divergence measures based on the Shannon entropy , 1991, IEEE Trans. Inf. Theory.
[66] Eric P. Xing,et al. Timeline: A Dynamic Hierarchical Dirichlet Process Model for Recovering Birth/Death and Evolution of Topics in Text Stream , 2010, UAI.
[67] Thomas Hofmann,et al. Probabilistic Latent Semantic Indexing , 1999, SIGIR Forum.
[68] Rachel Harrison,et al. Retrieving and analyzing mobile apps feature requests from online reviews , 2013, 2013 10th Working Conference on Mining Software Repositories (MSR).
[69] Xiaodong Gu,et al. Deep Code Search , 2018, 2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE).
[70] Diana Inkpen,et al. Semantic text similarity using corpus-based word similarity and string similarity , 2008, ACM Trans. Knowl. Discov. Data.
[71] Ying Zou,et al. Too Many User-Reviews! What Should App Developers Look at First? , 2019, IEEE Transactions on Software Engineering.
[72] Philip J. Guo,et al. Characterizing and predicting which bugs get fixed: an empirical study of Microsoft Windows , 2010, 2010 ACM/IEEE 32nd International Conference on Software Engineering.
[73] Michael R. Lyu,et al. Experience Report: Understanding Cross-Platform App Issues from User Reviews , 2016, 2016 IEEE 27th International Symposium on Software Reliability Engineering (ISSRE).
[74] Yulan He,et al. Joint sentiment/topic model for sentiment analysis , 2009, CIKM.
[75] Bastin Tony Roy Savarimuthu,et al. Attributes that Predict which Features to Fix: Lessons for App Store Mining , 2017, EASE.
[76] Rajarshi Das,et al. Gaussian LDA for Topic Models with Word Embeddings , 2015, ACL.
[77] Mia Hubert,et al. Robust statistics for outlier detection , 2011, WIREs Data Mining Knowl. Discov..
[78] Premkumar T. Devanbu,et al. When would this bug get reported? , 2012, 2012 28th IEEE International Conference on Software Maintenance (ICSM).
[79] Weizhong Zhao,et al. A heuristic approach to determine an appropriate number of topics in topic modeling , 2015, BMC Bioinformatics.