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Context: Mobile app reviews written by users on app stores or social media are significant resources for app developers.Analyzing app reviews have proved to be useful for many areas of software engineering (e.g., requirement engineering, testing).
1901
Earlier work this paper cites.
1906
Earlier work this paper cites.
1907
Earlier work this paper cites.
Rietzler A, Stabinger S, Opitz P, et al. Adapt or get left behind: Domain adaptation through BERT language model finetuning for aspecttarget sentiment classification. ArXiv: 1908.11860
1908
Earlier work this paper cites.
WuX, Zhang T, Zang L, et al. “Mask and infill”: Applying masked language model to sentiment transfer. ArXiv: 1908.08039
1908
Earlier work this paper cites.
1909
Earlier work this paper cites.
1909
Earlier work this paper cites.
Ju Y, Zhao F, Chen S, et al. Technical report on conversational question answering. ArXiv: 1909.10772
1909
Earlier work this paper cites.
Shen, V.Y., Yu, T.J., Thebaut, S.M. and Paulsen, L.R., 1985. Identifying error-prone software—an empirical study. IEEE Transactions on Software Engineering, (4), pp.317-324
1985
Earlier work this paper cites.
Hochreiter, S. and Schmidhuber, J., 1997. Long short-term memory. Neural computation, 9(8), pp.1735-1780
1997
Earlier work this paper cites.
Nigam, K., Lafferty, J. and McCallum, A., 1999, August. Using maximum entropy for text classification. In IJCAI-99 workshop on machine learning for information filtering (Vol. 1, No. 1, pp. 61-67)
1999
Earlier work this paper cites.
Zhang Z, Yang J, Zhao H. Retrospective reader for machine reading comprehension. ArXiv: 2001.09694
2001
Earlier work this paper cites.
Karimi A, Rossi L, Prati A, et al. Adversarial training for aspectbased sentiment analysis with BERT. ArXiv: 2001.11316 197 Song Y, Wang J, Liang Z, et al. Utilizing BERT intermediate layers for aspect based sentiment analysis and natural language inference. ArXiv: 2002.04815
2002
Earlier work this paper cites.
Blei, D.M., Ng, A.Y. and Jordan, M.I., 2003. Latent dirichlet allocation. the Journal of machine Learning research, 3, pp.993-1022
2003
Earlier work this paper cites.
Sun L, Hashimoto K, Yin W, et al. Adv-BERT: BERT is not robust on misspellings! Generating nature adversarial samples on BERT. ArXiv: 2003.04985
2003
Earlier work this paper cites.
LiL,MaR,GuoQ,etal. BERT-ATTACK:Adversarial attack against BERT using BERT. ArXiv: 2004.09984
2004
Earlier work this paper cites.
Liu X, Cheng H, He P C, et al. Adversarial training for large neural language models. ArXiv: 2004.08994
2004
Earlier work this paper cites.
2005
Earlier work this paper cites.
Islam, Dr. MD Rashedul and Mazumder, Tridib. (2010). Mobile application and its global impact. International Journal of Engineering and Technology. 10. 72-78
2010
Earlier work this paper cites.
2010
Earlier work this paper cites.
Forman, George, and Martin Scholz. ”Apples-to-apples in cross-validation studies: pitfalls in classifier performance measurement.” Acm Sigkdd Explorations Newsletter 12.1 (2010): 49-57
2010
Earlier work this paper cites.
Yatani, K., Novati, M., Trusty, A., and Truong, K. N. (2011, May). Review spotlight: a user interface for summarizing user-generated reviews using adjective-noun word pairs. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 1541-1550)
2011
Earlier work this paper cites.
Jo, Y. and Oh, A.H., 2011, February. Aspect and sentiment unification model for online review analysis. In Proceedings of the fourth ACM international conference on Web search and data mining (pp. 815-824)
2011
Earlier work this paper cites.
Joorabchi, M. E., Mesbah, A., and Kruchten, P. (2013, October). Real challenges in mobile app development. In 2013 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (pp. 15-24). IEEE
2013
Earlier work this paper cites.
He, Haibo, and Yunqian Ma, eds. ”Imbalanced learning: foundations, algorithms, and applications.” (2013)
2013
Earlier work this paper cites.
James, Gareth, et al. An introduction to statistical learning. Vol. 112. New York: springer, 2013
2013
Earlier work this paper cites.
in Fu, Jialiu Lin, Lei Li, Christos Faloutsos, Jason Hong, and Norman Sadeh. 2013. Why people hate your app: making sense of user feedback in a mobile app store. In ¡i¿Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining¡/i¿ (¡i¿KDD ’13¡/i¿). Association for Computing Machinery, New York, NY, USA, 1276–1284. DOI:https://doi.org/10.1145/2487575.2488202
2013
Earlier work this paper cites.
Carreno, L.V.G. and Winbladh, K., 2013, May. Analysis of user comments: an approach for software requirements evolution. In 2013 35th international conference on software engineering (ICSE) (pp. 582-591). IEEE
2013
Earlier work this paper cites.
Ning Chen, Jialiu Lin, Steven C. H. Hoi, Xiaokui Xiao, and Boshen Zhang. 2014. AR-miner: mining informative reviews for developers from mobile app marketplace. In Proceedings of the 36th International Conference on Software Engineering (ICSE 2014). Association for Computing Machinery, New York, NY, USA, 767–778. DOI:https://doi.org/10.1145/2568225.2568263
2014
Earlier work this paper cites.
Bavota, G., Linares-Vasquez, M., Bernal-Cardenas, C. E., Di Penta, M., Oliveto, R., and Poshyvanyk, D. (2014). The impact of api change-and fault-proneness on the user ratings of android apps. IEEE Transactions on Software Engineering, 41(4), 384-407
2014
Earlier work this paper cites.
Finkelstein, A., Harman, M., Jia, Y., Martin, W., Sarro, F., and Zhang, Y. (2014). App store analysis: Mining app stores for relationships between customer, business and technical characteristics. RN, 14(10), 24
2014
Earlier work this paper cites.
Guzman, E., and Maalej, W. (2014, August). How do users like this feature? a fine grained sentiment analysis of app reviews. In 2014 IEEE 22nd international requirements engineering conference (RE) (pp. 153-162). IEEE
2014
Earlier work this paper cites.
Pennington, J., Socher, R. and Manning, C.D., 2014, October. Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) (pp. 1532-1543)
2014
Earlier work this paper cites.
Wei Liu, Ge Zhang, Jun Chen, Yuze Zou, and Wenchao Ding. 2015. A Measurement-based Study on Application Popularity in Android and iOS App Stores. In ¡i¿Proceedings of the 2015 Workshop on Mobile Big Data¡/i¿ (¡i¿Mobidata ’15¡/i¿). Association for Computing Machinery, New York, NY, USA, 13–18. DOI:https://doi.org/10.1145/2757384.2757392
2015
Earlier work this paper cites.
W. Maalej and H. Nabil, ”Bug report, feature request, or simply praise? On automatically classifying app reviews,” 2015 IEEE 23rd International Requirements Engineering Conference (RE), 2015, pp. 116-125, doi: 10.1109/RE.2015.7320414
2015
Earlier work this paper cites.
X. Gu and S. Kim, ”What Parts of Your Apps are Loved by Users?” (T),” 2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE), 2015, pp. 760-770, doi: 10.1109/ASE.2015.57
2015
Earlier work this paper cites.
E. Guzman, M. El-Haliby and B. Bruegge, ”Ensemble Methods for App Review Classification: An Approach for Software Evolution (N),” 2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE), 2015, pp. 771-776, doi: 10.1109/ASE.2015.88
2015
Earlier work this paper cites.
Panichella, S., Di Sorbo, A., Guzman, E., Visaggio, C. A., Canfora, G., and Gall, H. C. (2015, September). How can i improve my app? classifying user reviews for software maintenance and evolution. In 2015 IEEE international conference on software maintenance and evolution (ICSME) (pp. 281-290). IEEE
2015
Earlier work this paper cites.
Palomba, F., Linares-Vásquez, M., Bavota, G., Oliveto, R., Di Penta, M., Poshyvanyk, D., and De Lucia, A. (2015, September). User reviews matter! tracking crowdsourced reviews to support evolution of successful apps. In 2015 IEEE international conference on software maintenance and evolution (ICSME) (pp. 291-300). IEEE
2015
Earlier work this paper cites.
Moran, K., Linares-Vásquez, M., Bernal-Cárdenas, C., and Poshyvanyk, D. (2015, August). Auto-completing bug reports for android applications. In Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering (pp. 673-686)
2015
Earlier work this paper cites.
Sarro, F., Al-Subaihin, A. A., Harman, M., Jia, Y., Martin, W., and Zhang, Y. (2015, August). Feature lifecycles as they spread, migrate, remain, and die in app stores. In 2015 IEEE 23rd International Requirements Engineering Conference (RE) (pp. 76-85). IEEE
2015
Earlier work this paper cites.
Villarroel, L., Bavota, G., Russo, B., Oliveto, R., and Di Penta, M. (2016, May). Release planning of mobile apps based on user reviews. In 2016 IEEE/ACM 38th International Conference on Software Engineering (ICSE) (pp. 14-24). IEEE
2016
Earlier work this paper cites.
Di Sorbo, A., Panichella, S., Alexandru, C. V., Shimagaki, J., Visaggio, C. A., Canfora, G., and Gall, H. C. (2016, November). What would users change in my app? summarizing app reviews for recommending software changes. In Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering (pp. 499-510)
2016
Earlier work this paper cites.
Moran, K., Linares-Vásquez, M., Bernal-Cárdenas, C., Vendome, C., and Poshyvanyk, D. (2016, April). Automatically discovering, reporting and reproducing android application crashes. In 2016 IEEE international conference on software testing, verification and validation (icst) (pp. 33-44). IEEE
2016
Earlier work this paper cites.
Martin, W., Sarro, F., Jia, Y., Zhang, Y., and Harman, M. (2016). A survey of app store analysis for software engineering. IEEE transactions on software engineering, 43(9), 817-847
2016
Earlier work this paper cites.
Guzman, E., Alkadhi, R., and Seyff, N. (2016, September). A needle in a haystack: What do twitter users say about software?. In 2016 IEEE 24th international requirements engineering conference (RE) (pp. 96-105). IEEE
2016
Earlier work this paper cites.
Maalej, W., Kurtanović, Z., Nabil, H., and Stanik, C. (2016). On the automatic classification of app reviews. Requirements Engineering, 21(3), 311-331
2016
Earlier work this paper cites.
Ren, Y., Zhang, Y., Zhang, M., and Ji, D. (2016, March). Improving twitter sentiment classification using topic-enriched multi-prototype word embeddings. In Thirtieth AAAI conference on artificial intelligence
2016
Earlier work this paper cites.
Rajpurkar P, Zhang J, Lopyrev K, et al. Squad: 100, 000+ questions for machine comprehension of text. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. Austin, 2016. 2383–2392
2016
Earlier work this paper cites.
Mengmeng Lu and Peng Liang. 2017. Automatic Classification of Non-Functional Requirements from Augmented App User Reviews. In Proceedings of the 21st International Conference on Evaluation and Assessment in Software Engineering (EASE’17). Association for Computing Machinery, New York, NY, USA, 344–353. DOI:https://doi.org/10.1145/3084226.3084241
2017
Earlier work this paper cites.
A. Ciurumelea, A. Schaufelbühl, S. Panichella and H. C. Gall, ”Analyzing reviews and code of mobile apps for better release planning,” 2017 IEEE 24th International Conference on Software Analysis, Evolution and Reengineering (SANER), 2017, pp. 91-102, doi: 10.1109/SANER.2017.7884612
2017
Earlier work this paper cites.
Roger Deocadez, Rachel Harrison, and Daniel Rodriguez. 2017. Preliminary Study on Applying Semi-Supervised Learning to App Store Analysis. In Proceedings of the 21st International Conference on Evaluation and Assessment in Software Engineering (EASE’17). Association for Computing Machinery, New York, NY, USA, 320–323. DOI:https://doi.org/10.1145/3084226.3084285
2017
Cited alongside, same era.
Ruder, S., and Plank, B. (2017). Learning to select data for transfer learning with Bayesian Optimization. EMNLP
2017
Cited alongside, same era.
Johann, T., Stanik, C., and Maalej, W. (2017, September). Safe: A simple approach for feature extraction from app descriptions and app reviews. In 2017 IEEE 25th International Requirements Engineering Conference (RE) (pp. 21-30). IEEE
2017
Cited alongside, same era.
Di Sorbo, A., Panichella, S., Alexandru, C. V., Visaggio, C. A., and Canfora, G. (2017, May). SURF: summarizer of user reviews feedback. In 2017 IEEE/ACM 39th International Conference on Software Engineering Companion (ICSE-C) (pp. 55-58). IEEE
Robbes, R. and Janes, A., 2019, May. Leveraging small software engineering data sets with pre-trained neural networks. In 2019 IEEE/ACM 41st International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER) (pp. 29-32). IEEE
2019
Later among the works it cites.
Messaoud, M.B., Jenhani, I., Jemaa, N.B. and Mkaouer, M.W., 2019, August. A multi-label active learning approach for mobile app user review classification. In International Conference on Knowledge Science, Engineering and Management (pp. 805-816). Springer, Cham
2019
Later among the works it cites.
Guo, B., Ouyang, Y., Guo, T., Cao, L. and Yu, Z., 2019. Enhancing mobile app user understanding and marketing with heterogeneous crowdsourced data: a review. IEEE Access, 7, pp.68557-68571
2019
Later among the works it cites.
He, D., Hong, K., Cheng, Y., Tang, Z. and Guizani, M., 2019. Detecting Promotion Attacks in the App Market Using Neural Networks. IEEE Wireless Communications, 26(4), pp.110-116
2019
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alphaXiv is searching for related work…
2017
Cited alongside, same era.
Palomba, F., Salza, P., Ciurumelea, A., Panichella, S., Gall, H., Ferrucci, F., and De Lucia, A. (2017, May). Recommending and localizing change requests for mobile apps based on user reviews. In 2017 IEEE/ACM 39th International Conference on Software Engineering (ICSE) (pp. 106-117). IEEE
2017
Cited alongside, same era.
Bakiu, E., and Guzman, E. (2017, September). Which feature is unusable? Detecting usability and user experience issues from user reviews. In 2017 IEEE 25th International Requirements Engineering Conference Workshops (REW) (pp. 182-187). IEEE
2017
Cited alongside, same era.
Guzman, E., Ibrahim, M., and Glinz, M. (2017, September). A little bird told me: Mining tweets for requirements and software evolution. In 2017 IEEE 25th International Requirements Engineering Conference (RE) (pp. 11-20). IEEE
2017
Cited alongside, same era.
Ali, M., Joorabchi, M. E., and Mesbah, A. (2017, May). Same app, different app stores: A comparative study. In 2017 IEEE/ACM 4th International Conference on Mobile Software Engineering and Systems (MOBILESoft) (pp. 79-90). IEEE
2017
Cited alongside, same era.
Scalabrino, S., Bavota, G., Russo, B., Di Penta, M., and Oliveto, R. (2017). Listening to the crowd for the release planning of mobile apps. IEEE Transactions on Software Engineering, 45(1), 68-86
2017
Cited alongside, same era.
Stade, M., Fotrousi, F., Seyff, N., and Albrecht, O. (2017, September). Feedback gathering from an industrial point of view. In 2017 IEEE 25th International Requirements Engineering Conference (RE) (pp. 71-79). IEEE
2017
Cited alongside, same era.
Guzman, Emitza, Rana Alkadhi, and Norbert Seyff. ”An exploratory study of Twitter messages about software applications.” Requirements Engineering 22.3 (2017): 387-412
2017
Cited alongside, same era.
Zhao, Wei, et al. ”Weakly-supervised deep embedding for product review sentiment analysis.” IEEE Transactions on Knowledge and Data Engineering 30.1 (2017): 185-197
2017
Cited alongside, same era.
Later among the works it cites.
Zhao, L. and Zhao, A., 2019. Sentiment analysis based requirement evolution prediction. Future Internet, 11(2), p.52
2019
Later among the works it cites.
Reddy S, Chen D, Manning C D. CoQA: A conversational question answering challenge. Trans Associat Comput Linguist, 2019, 7: 249266
2019
Later among the works it cites.
Bataa E, Wu J. An investigation of transfer learning-based sentiment analysis in japanese. In: Proceedings of the Conference of the Association for Computational Linguistics. Florence, 2019. 4652–4657
2019
Later among the works it cites.
Sun C, Huang L, Qiu X. Utilizing BERT for aspect-based sentiment analysis via constructing auxiliary sentence. In: Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Minneapolis, 2019. 380–385
2019
Later among the works it cites.
Xu H, Liu B, Shu L, et al. BERT post-training for review reading comprehension and aspect-based sentiment analysis. In: Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Minneapolis, 2019. 2324–2335
2019
Later among the works it cites.
Li X, Bing L, Zhang W, et al. Exploiting BERT for end-toend aspect-based sentiment analysis. In: Proceedings of the WNUT@Conference on Empirical Methods in Natural Language Processing. Hong Kong, 2019. 34–41
2019
Later among the works it cites.
Hakala K, Pyysalo S. Biomedical named entity recognition with multilingual BERT. In: Proceedings of the BioNLP Open Shared Tasks@Conference on Empirical Methods in Natural Language Processing. Hong Kong, 2019. 56–61
2019
Later among the works it cites.
Edunov S, Baevski A, Auli M. Pre-trained language model representations for language generation. In: Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Minneapolis, 2019. 4052–4059
2019
Later among the works it cites.
Clinchant S, Jung K W, Nikoulina V. On the use of BERT for neural machine translation. In: Proceedings of the Proceedings of the 3rd Workshop on Neural Generation and Translation. Hong Kong, 2019. 108–117
2019
Later among the works it cites.
Imamura K, Sumita E. Recycling a pre-trained BERT encoder for neural machine translation. In: Proceedings of the 3rd Workshop on Neural Generation and Translation. Hong Kong, 2019. 23–31
2019
Later among the works it cites.
Zhang X, Wei F, Zhou M. HIBERT: Document level pre-training of hierarchical bidirectional transformers for document summarization. In: Proceedings of the Conference of the Association for Computational Linguistics. Florence, 2019. 5059–5069
2019
Later among the works it cites.
Liu Y, Lapata M. Text summarization with pretrained encoders. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. Hong Kong, 2019. 3728–3738
2019
Later among the works it cites.
Wallace E, Feng S, Kandpal N, et al. Universal adversarial triggers for attacking and analyzing NLP. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. Hong Kong, 2019. 2153–2162
2019
Later among the works it cites.
Al-Hawari, A., Najadat, H. and Shatnawi, R. Classification of application reviews into software maintenance tasks using data mining techniques. Software Qual J (2020). https://doi.org/10.1007/s11219-020-09529-8
2020
Later among the works it cites.
Andrew R. Besmer and Jason Watson and M. Shane Banks, 2020. ”Investigating User Perceptions of Mobile App Privacy: An Analysis of User-Submitted App Reviews,” International Journal of Information Security and Privacy (IJISP), IGI Global, vol. 14(4), pages 74-91, October
2020
Later among the works it cites.
T. Zhang, B. Xu, F. Thung, S. A. Haryono, D. Lo and L. Jiang, ”Sentiment Analysis for Software Engineering: How Far Can Pre-trained Transformer Models Go?,” 2020 IEEE International Conference on Software Maintenance and Evolution (ICSME), 2020, pp. 70-80, doi: 10.1109/ICSME46990.2020.00017
2020
Later among the works it cites.
E. Biswas, M. E. Karabulut, L. Pollock and K. Vijay-Shanker, ”Achieving Reliable Sentiment Analysis in the Software Engineering Domain using BERT,” 2020 IEEE International Conference on Software Maintenance and Evolution (ICSME), 2020, pp. 162-173, doi: 10.1109/ICSME46990.2020.00025
2020
Later among the works it cites.
N. Aslam, W. Y. Ramay, K. Xia and N. Sarwar, ”Convolutional Neural Network Based Classification of App Reviews,” in IEEE Access, vol. 8, pp. 185619-185628, 2020, doi: 10.1109/ACCESS.2020.3029634
2020
Later among the works it cites.
Mohammad Abdul Hadi and Fatemeh Hendijani Fard. 2020. ReviewViz: assisting developers perform empirical study on energy consumption related reviews for mobile applications. In ¡i¿Proceedings of the IEEE/ACM 7th International Conference on Mobile Software Engineering and Systems¡/i¿ (¡i¿MOBILESoft ’20¡/i¿). Association for Computing Machinery, New York, NY, USA, 27–30. DOI:https://doi.org/10.1145/3387905.3388605
2020
Later among the works it cites.
Qiu, X., Sun, T., Xu, Y., Shao, Y., Dai, N., and Huang, X. (2020). Pre-trained models for natural language processing: A survey. Science China Technological Sciences, 1-26
2020
Later among the works it cites.
Svyatkovskiy, A., Deng, S. K., Fu, S., and Sundaresan, N. (2020, November). Intellicode compose: Code generation using transformer. In Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (pp. 1433-1443)
2020
Later among the works it cites.
Hadi, M. A., and Fard, F. H. (2020, September). AOBTM: Adaptive Online Biterm Topic Modeling for Version Sensitive Short-texts Analysis. In 2020 IEEE International Conference on Software Maintenance and Evolution (ICSME) (pp. 593-604). IEEE
2020
Later among the works it cites.
Guo, H., and Singh, M. P. (2020, October). Caspar: extracting and synthesizing user stories of problems from app reviews. In 2020 IEEE/ACM 42nd International Conference on Software Engineering (ICSE) (pp. 628-640). IEEE
2020
Later among the works it cites.
Stanik, C. (2020). Requirements Intelligence: On the Analysis of User Feedback (Doctoral dissertation, Staats-und Universitätsbibliothek Hamburg Carl von Ossietzky)
2020
Later among the works it cites.
M. A. Hadi and F. H. Fard, ”AOBTM: Adaptive Online Biterm Topic Modeling for Version Sensitive Short-texts Analysis,” 2020 IEEE International Conference on Software Maintenance and Evolution (ICSME), 2020, pp. 593-604, doi: 10.1109/ICSME46990.2020.00062
2020
Later among the works it cites.
Qiu, X., Sun, T., Xu, Y., Shao, Y., Dai, N. and Huang, X., 2020. Pre-trained models for natural language processing: A survey. Science China Technological Sciences, pp.1-26
2020
Later among the works it cites.
Sulistya, A., Prana, G.A.A., Sharma, A., Lo, D. and Treude, C., 2020. Sieve: Helping developers sift wheat from chaff via cross-platform analysis. Empirical Software Engineering, 25(1), pp.996-1030
2020
Later among the works it cites.
Rustam, F., Mehmood, A., Ahmad, M., Ullah, S., Khan, D.M. and Choi, G.S., 2020. Classification of shopify app user reviews using novel multi text features. IEEE Access, 8, pp.30234-30244
2020
Later among the works it cites.
Triantafyllou, I., Drivas, I.C. and Giannakopoulos, G., 2020. How to Utilize my App Reviews? A Novel Topics Extraction Machine Learning Schema for Strategic Business Purposes. Entropy, 22(11), p.1310
2020
Later among the works it cites.
Qiao, Z., Wang, A., Abrahams, A. and Fan, W., 2020. Deep Learning-Based User Feedback Classification in Mobile App Reviews
2020
Later among the works it cites.
Wang, J., Wen, R., Wu, C. and Xiong, J., 2020, April. Analyzing and Detecting Adversarial Spam on a Large-scale Online APP Review System. In Companion Proceedings of the Web Conference 2020 (pp. 409-417)
2020
Later among the works it cites.
Tu M, Huang K, Wang G, et al. Select, answer and explain: Interpretable multi-hop reading comprehension over multiple documents. In: Proceedings of the AAAI Conference on Artificial Intelligence. New York, 2020. 9073–9080
2020
Later among the works it cites.
Zhong M, Liu P, Chen Y, et al. Extractive summarization as text matching. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics. Online, 2020. 6197–6208
2020
Later among the works it cites.
Jin D, Jin Z, Zhou J T, et al. Is BERT really robust? Natural language attack on text classification and entailment. In: Proceedings of the AAAI Conference on Artificial Intelligence. New York, 2020. 8018–8025
2020
Later among the works it cites.
ZhuC,ChengY,GanZ,etal. FreeLB: Enhanced adversarial training for natural language understanding. In: Proceedings of the International Conference on Learning Representations. Addis Ababa, 2020
2020
Later among the works it cites.
Singh, R. K., Sachan, M. K., and Patel, R. B. (2021). 360 degree view of cross-domain opinion classification: a survey. Artificial Intelligence Review, 54(2), 1385-1506
2021
Closest in time.
Sarzynska-Wawer, J., Wawer, A., Pawlak, A., Szymanowska, J., Stefaniak, I., Jarkiewicz, M. and Okruszek, L., 2021. Detecting formal thought disorder by deep contextualized word representations. Psychiatry Research, 304, p.114135
2021
Closest in time.
Subedi, I.M., Singh, M., Ramasamy, V. and Walia, G.S., 2021, April. Application of back-translation: a transfer learning approach to identify ambiguous software requirements. In Proceedings of the 2021 ACM Southeast Conference (pp. 130-137)
2021
Closest in time.
Silva, C.C., Galster, M. and Gilson, F., 2021. Topic modeling in software engineering research. Empirical Software Engineering, 26(6), pp.1-62
2021
Closest in time.
Yang, T., Gao, C., Zang, J., Lo, D. and Lyu, M., 2021, April. TOUR: Dynamic Topic and Sentiment Analysis of User Reviews for Assisting App Release. In Companion Proceedings of the Web Conference 2021 (pp. 708-712)
2021
Closest in time.
Wardhana, J.A. and Sibaroni, Y., 2021. Aspect Level Sentiment Analysis on Zoom Cloud Meetings App Review Using LDA. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(4), pp.631-638
2021
Closest in time.
Fu, L., Liang, P., Li, X. and Yang, C., 2021. A Machine Learning Based Ensemble Method for Automatic Multiclass Classification of Decisions. In Evaluation and Assessment in Software Engineering (pp. 40-49)
2021
Closest in time.
Mekala, R.R., Irfan, A., Groen, E.C., Porter, A. and Lindvall, M., 2021, September. Classifying user requirements from online feedback in small dataset environments using deep learning. In 2021 IEEE 29th International Requirements Engineering Conference (RE) (pp. 139-149). IEEE
2021
Closest in time.
Henao, P.R., Fischbach, J., Spies, D., Frattini, J. and Vogelsang, A., 2021, September. Transfer Learning for Mining Feature Requests and Bug Reports from Tweets and App Store Reviews. In 2021 IEEE 29th International Requirements Engineering Conference Workshops (REW) (pp. 80-86). IEEE
2021
Closest in time.
Haering, M., Stanik, C. and Maalej, W., 2021, May. Automatically Matching Bug Reports With Related App Reviews. In 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) (pp. 970-981). IEEE
2021
Closest in time.