Rant or rave: variation over time in the language of online reviews
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[1] Shay B. Cohen,et al. Understanding Domain Learning in Language Models Through Subpopulation Analysis , 2022, BLACKBOXNLP.
[2] Roi Reichart,et al. Domain Adaptation from Scratch , 2022, ArXiv.
[3] A. Zubiaga,et al. Building for Tomorrow: Assessing the Temporal Persistence of Text Classifiers , 2022, Inf. Process. Manag..
[4] Roi Reichart,et al. DILBERT: Customized Pre-Training for Domain Adaptation with Category Shift, with an Application to Aspect Extraction , 2021, EMNLP.
[5] Chenhao Tan,et al. On Positivity Bias in Negative Reviews , 2021, ACL.
[6] Manit Mishra,et al. BERT: a sentiment analysis odyssey , 2021, Journal of Marketing Analytics.
[7] Vijay Mago,et al. TweetBERT: A Pretrained Language Representation Model for Twitter Text Analysis , 2020, ArXiv.
[8] Stephen A. Rains,et al. Perceptions of Uncivil Discourse Online: An Examination of Types and Predictors , 2020 .
[9] Davide Proserpio,et al. The Market for Fake Reviews , 2020, EC.
[10] Florian Stahl,et al. The Polarity of Online Reviews: Prevalence, Drivers and Implications , 2020, Journal of Marketing Research.
[11] James Caverlee,et al. Next-item Recommendation with Sequential Hypergraphs , 2020, SIGIR.
[12] Roi Reichart,et al. PERL: Pivot-based Domain Adaptation for Pre-trained Deep Contextualized Embedding Models , 2020, Transactions of the Association for Computational Linguistics.
[13] Eric W.T. Ngai,et al. Fake online reviews: Literature review, synthesis, and directions for future research , 2020, Decis. Support Syst..
[14] Fei Liu,et al. Sentiment analysis: dynamic and temporal clustering of product reviews , 2020, Applied Intelligence.
[15] Jianmo Ni,et al. Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects , 2019, EMNLP.
[16] Wouter M. Kouw,et al. Back to the Future - Sequential Alignment of Text Representations , 2019, ArXiv.
[17] Yu Qian,et al. Users' Opinions in Online Financial Community and Its Impact on the Market , 2019, 2019 16th International Conference on Service Systems and Service Management (ICSSSM).
[18] Philip S. Yu,et al. BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis , 2019, NAACL.
[19] Muhammad Rifki Shihab,et al. Negative online reviews of popular products: understanding the effects of review proportion and quality on consumers’ attitude and intention to buy , 2019, Electron. Commer. Res..
[20] Ulrike Gretzel,et al. Online reviews: Differences by submission device , 2019, Tourism Management.
[21] Jianan Wu,et al. How Online Reviews Become Helpful: A Dynamic Perspective , 2018, Journal of Interactive Marketing.
[22] Cristian Danescu-Niculescu-Mizil,et al. WikiConv: A Corpus of the Complete Conversational History of a Large Online Collaborative Community , 2018, EMNLP.
[23] Xu Chen,et al. Explainable Recommendation: A Survey and New Perspectives , 2018, Found. Trends Inf. Retr..
[24] S. Wahyuningsih. MEN AND WOMEN DIFFERENCES IN USING LANGUAGE: A CASE STUDY OF STUDENTS AT STAIN KUDUS , 2018 .
[25] E. Papaioannou,et al. The European Trust Crisis and the Rise of Populism , 2017 .
[26] Peter Holtz,et al. Cross-Cultural Psychology and the Rise of Academic Capitalism: Linguistic Changes in CCR and JCCP Articles, 1970-2014 , 2017 .
[27] Yulia Tsvetkov,et al. Incorporating Dialectal Variability for Socially Equitable Language Identification , 2017, ACL.
[28] Regina Jucks,et al. Better to have many opinions than one from an expert? Social validation by one trustworthy source versus the masses in online health forums , 2017, Comput. Hum. Behav..
[29] Fang Wang,et al. Online review helpfulness: Impact of reviewer profile image , 2017, Decis. Support Syst..
[30] Jure Leskovec,et al. Loyalty in Online Communities , 2017, ICWSM.
[31] Jan H. Schumann,et al. “Why Would I Read a Mobile Review?” Device Compatibility Perceptions and Effects on Perceived Helpfulness , 2017 .
[32] Roi Reichart,et al. Neural Structural Correspondence Learning for Domain Adaptation , 2016, CoNLL.
[33] John G. Breslin,et al. A Hierarchical Model of Reviews for Aspect-based Sentiment Analysis , 2016, EMNLP.
[34] Gordon H. Hanson,et al. Importing Political Polarization? The Electoral Consequences of Rising Trade Exposure , 2016, American Economic Review.
[35] Brendan T. O'Connor,et al. Demographic Dialectal Variation in Social Media: A Case Study of African-American English , 2016, EMNLP.
[36] Dirk Hovy,et al. Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter , 2016, NAACL.
[37] J. Grieve,et al. Analyzing lexical emergence in Modern American English online 1 , 2016, English Language and Linguistics.
[38] Nicole L. Exe,et al. One-Sided Social Media Comments Influenced Opinions And Intentions About Home Birth: An Experimental Study. , 2016, Health affairs.
[39] Willem M Otte,et al. Use of positive and negative words in scientific PubMed abstracts between 1974 and 2014: retrospective analysis , 2015, British Medical Journal.
[40] Matthew Purver,et al. Twitter Language Use Reflects Psychological Differences between Democrats and Republicans , 2015, PloS one.
[41] Alessandro Moschitti,et al. UNITN: Training Deep Convolutional Neural Network for Twitter Sentiment Classification , 2015, *SEMEVAL.
[42] Georgios Zervas,et al. Fake It Till You Make It: Reputation, Competition, and Yelp Review Fraud , 2015, Manag. Sci..
[43] Jacob Eisenstein,et al. AUDIENCE-MODULATED VARIATION IN ONLINE SOCIAL MEDIA , 2015 .
[44] Suresh Manandhar,et al. SemEval-2014 Task 4: Aspect Based Sentiment Analysis , 2014, *SEMEVAL.
[45] James P. Bagrow,et al. Human language reveals a universal positivity bias , 2014, Proceedings of the National Academy of Sciences.
[46] Eric Gilbert,et al. VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text , 2014, ICWSM.
[47] Yang Li,et al. Interpreting the Public Sentiment Variations on Twitter , 2014, IEEE Transactions on Knowledge and Data Engineering.
[48] Hinrich Schütze,et al. FLORS: Fast and Simple Domain Adaptation for Part-of-Speech Tagging , 2014, TACL.
[49] Arthur D. Santana. Virtuous or Vitriolic , 2014 .
[50] Li Wang,et al. How Noisy Social Media Text, How Diffrnt Social Media Sources? , 2013, IJCNLP.
[51] Margaret L. Kern,et al. Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach , 2013, PloS one.
[52] Ian Rowlands,et al. Information on the go: A case study of Europeana mobile users , 2013, J. Assoc. Inf. Sci. Technol..
[53] Jacob Eisenstein,et al. What to do about bad language on the internet , 2013, NAACL.
[54] Jure Leskovec,et al. No country for old members: user lifecycle and linguistic change in online communities , 2013, WWW.
[55] Brendan T. O'Connor,et al. Diffusion of Lexical Change in Social Media , 2012, PloS one.
[56] Rie Koizumi,et al. Relationships between text length and lexical diversity measures: Can we use short texts of less than 100 tokens? , 2012 .
[57] Theodoros Lappas,et al. Fake Reviews: The Malicious Perspective , 2012, NLDB.
[58] R. Schindler,et al. Perceived helpfulness of online consumer reviews: The role of message content and style: Perceived helpfulness of online consumer reviews , 2012 .
[59] Bing Liu,et al. Sentiment Analysis and Opinion Mining , 2012, Synthesis Lectures on Human Language Technologies.
[60] Clement T. Yu,et al. Topic Sentiment Change Analysis , 2011, MLDM.
[61] Finn Årup Nielsen,et al. A New ANEW: Evaluation of a Word List for Sentiment Analysis in Microblogs , 2011, #MSM.
[62] Fagan Stephen,et al. An Introduction to Textual Econometrics , 2010 .
[63] Christopher S. G. Khoo,et al. Aspect-based sentiment analysis of movie reviews on discussion boards , 2010, J. Inf. Sci..
[64] Brendan T. O'Connor,et al. A Latent Variable Model for Geographic Lexical Variation , 2010, EMNLP.
[65] Tejashri Inadarchand Jain,et al. Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis , 2010 .
[66] Alcides Velasquez,et al. Motivations to participate in online communities , 2010, CHI.
[67] Shintaro Okazaki. Social influence model and electronic word of mouth , 2009 .
[68] Kyung Hyan Yoo,et al. What Motivates Consumers to Write Online Travel Reviews? , 2008, J. Inf. Technol. Tour..
[69] Janyce Wiebe,et al. Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis , 2005, HLT.
[70] Y. Benjamini,et al. False Discovery Rate–Adjusted Multiple Confidence Intervals for Selected Parameters , 2005 .
[71] John Suler,et al. The Online Disinhibition Effect , 2004, Cyberpsychology Behav. Soc. Netw..
[72] S. Wilson,et al. The Anthropology of Online Communities , 2002 .
[73] Jean Aitchison,et al. Language and the Internet , 2002, Lit. Linguistic Comput..
[74] Abigail Sellen,et al. How knowledge workers use the web , 2002, CHI.
[75] David Malvern,et al. Measuring vocabulary diversity using dedicated software , 2000 .
[76] A. Ziv,et al. Teaching and learning with humor: Experiment and replication. , 1988 .
[77] C. Osgood,et al. The Pollyanna hypothesis. , 1969 .
[78] H. B. Mann. Nonparametric Tests Against Trend , 1945 .
[79] George Kingsley Zipf,et al. The Unity of Nature, Least-Action, and Natural Social Science , 1942 .
[80] P. Resnik,et al. Bernice: A Multilingual Pre-trained Encoder for Twitter , 2022, EMNLP.
[81] OUP accepted manuscript , 2021, Applied Linguistics.
[82] F. Sadat,et al. On the Hidden Negative Transfer in Sequential Transfer Learning for Domain Adaptation from News to Tweets , 2021, ADAPTNLP.
[83] Khaled Shaalan,et al. Using Artificial Intelligence to Understand What Causes Sentiment Changes on Social Media , 2021, IEEE Access.
[84] A. Aggarwal,et al. Analysing the interrelationship between online reviews and sales: the role of review length and sentiment index in electronic markets , 2020, International Journal of Internet Marketing and Advertising.
[85] Ming-Wei Chang,et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.
[86] B. S. Harish,et al. Sentiment Analysis on IMDb Movie Reviews Using Hybrid Feature Extraction Method , 2019, Int. J. Interact. Multim. Artif. Intell..
[87] Sentiment Analysis for IMDb Movie Review , 2019 .
[88] Matthew Gentzkow,et al. Polarization in 2016 , 2016 .
[89] Bernd Kortmann,et al. Analyzing lexical emergence in Modern American English online 1 , 2016, English Language and Linguistics.
[90] Mohammad Salehan,et al. Predicting the performance of online consumer reviews: A sentiment mining approach to big data analytics , 2014, Decis. Support Syst..
[91] Georg Lackermair,et al. Importance of Online Product Reviews from a Consumer's Perspective , 2013 .
[92] Fabio Massimo Zanzotto,et al. Language Evolution in Social Media: a Preliminary Study , 2012 .
[93] Qing Cao,et al. Exploring determinants of voting for the "helpfulness" of online user reviews: A text mining approach , 2011, Decis. Support Syst..
[94] S. Cuéllar,et al. Translation quality assessment. A model revisited , 2002 .
[95] R. Forthofer,et al. Rank Correlation Methods , 1981 .