A framework for big data analytics in commercial social networks: A case study on sentiment analysis and fake review detection for marketing decision-making

[1]  Kristof Coussement,et al.  Acceptance of text-mining systems: The signaling role of information quality , 2018, Inf. Manag..

[2]  Shu-Hui Chuang,et al.  Co-creating social media agility to build strong customer-firm relationships , 2020 .

[3]  Ravi Shankar,et al.  A big data driven framework for demand-driven forecasting with effects of marketing-mix variables , 2019 .

[4]  Bo Pang,et al.  A unified framework for detecting author spamicity by modeling review deviation , 2018, Expert Syst. Appl..

[5]  Jyoti Prakash Singh,et al.  Detection of spam reviews: a sentiment analysis approach , 2018, CSI Transactions on ICT.

[6]  Nicole Novielli,et al.  A Benchmark Study on Sentiment Analysis for Software Engineering Research , 2018, 2018 IEEE/ACM 15th International Conference on Mining Software Repositories (MSR).

[7]  Shahaboddin Shamshirband,et al.  Machine Learning-Based Sentiment Analysis for Twitter Accounts , 2018 .

[8]  Paulo Rita,et al.  Research trends on Big Data in Marketing: A text mining and topic modeling based literature analysis , 2018, European Research on Management and Business Economics.

[9]  Sepideh Paknejad,et al.  Sentiment classification on Amazon reviews using machine learning approaches , 2018 .

[10]  Jie Cao,et al.  Detecting Spammer Groups From Product Reviews: A Partially Supervised Learning Model , 2018, IEEE Access.

[11]  Marleen Huysman,et al.  Debating big data: A literature review on realizing value from big data , 2017, J. Strateg. Inf. Syst..

[12]  Shah Jahan Miah,et al.  A Big Data Analytics Method for Tourist Behaviour Analysis , 2017, Inf. Manag..

[13]  Han Liu,et al.  Fuzzy approach for sentiment analysis , 2017, 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[14]  C Sindhu,et al.  Sentiment analysis based product rating using textual reviews , 2017, 2017 International conference of Electronics, Communication and Aerospace Technology (ICECA).

[15]  A. Burns,et al.  An Investigation of Brand-Related User-Generated Content on Twitter , 2017 .

[16]  Dr.R.Mahalakshmi.  A study on detecting Opinion Spam Concerning the Issues, Challenges and Opportunities. , 2017 .

[17]  Abdelouahed Gherbi,et al.  Detecting Fake Reviews through Sentiment Analysis Using Machine Learning Techniques , 2017 .

[18]  Z. Irani,et al.  Critical analysis of Big Data challenges and analytical methods , 2017 .

[19]  Vijay V. Raghavan,et al.  Big Data and Data Analytics Research: From Metaphors to Value Space for Collective Wisdom in Human Decision Making and Smart Machines , 2017, Int. J. Semantic Web Inf. Syst..

[20]  Zeshui Xu,et al.  Towards felicitous decision making: An overview on challenges and trends of Big Data , 2016, Inf. Sci..

[21]  M. Wedel,et al.  Marketing Analytics for Data-Rich Environments , 2016 .

[22]  Naomie Salim,et al.  Detection of fake opinions using time series , 2016, Expert Syst. Appl..

[23]  Padma Dandannavar,et al.  Application of machine learning techniques to sentiment analysis , 2016, 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT).

[24]  David Robinson,et al.  tidytext: Text Mining and Analysis Using Tidy Data Principles in R , 2016, J. Open Source Softw..

[25]  Erik Cambria,et al.  Affective Computing and Sentiment Analysis , 2016, IEEE Intelligent Systems.

[26]  Julian J. McAuley,et al.  Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering , 2016, WWW.

[27]  Sunil Erevelles,et al.  Big Data consumer analytics and the transformation of marketing , 2016 .

[28]  Arif Djunaidy,et al.  FAKE REVIEW DETECTION FROM A PRODUCT REVIEW USING MODIFIED METHOD OF ITERATIVE COMPUTATION FRAMEWORK , 2016 .

[29]  Hilal Ahmad Khanday,et al.  Spam Detection : A Review , 2016 .

[30]  Christos Faloutsos,et al.  Suspicious Behavior Detection: Current Trends and Future Directions , 2016, IEEE Intelligent Systems.

[31]  Wu He,et al.  A novel social media competitive analytics framework with sentiment benchmarks , 2015, Inf. Manag..

[32]  Naomie Salim,et al.  A Framework for Review Spam Detection Research , 2015 .

[33]  Junjie Wu,et al.  Spammers Detection from Product Reviews: A Hybrid Model , 2015, 2015 IEEE International Conference on Data Mining.

[34]  Leman Akoglu,et al.  Discovering Opinion Spammer Groups by Network Footprints , 2015, ECML/PKDD.

[35]  Christian Homburg,et al.  Measuring and Managing Consumer Sentiment in an Online Community Environment , 2015 .

[36]  Hua Xu,et al.  A rule-based approach to emotion cause detection for Chinese micro-blogs , 2015, Expert Syst. Appl..

[37]  Naomie Salim,et al.  Detection of review spam: A survey , 2015, Expert Syst. Appl..

[38]  Raymond Y. K. Lau,et al.  Demystifying Big Data Analytics for Business Intelligence Through the Lens of Marketing Mix , 2015, Big Data Res..

[39]  Chetashri Bhadane,et al.  Sentiment Analysis: Measuring Opinions , 2015 .

[40]  Kolli Shivagangadhar,et al.  Fraud Detection in Online Reviews using Machine Learning Techniques , 2015 .

[41]  Breffni M. Noone,et al.  Effects of Price and User-Generated Content on Consumers’ Prepurchase Evaluations of Variably Priced Services , 2014 .

[42]  A. Sathi.  Engaging Customers Using Big Data: How Marketing Analytics Are Transforming Business , 2014 .

[43]  Claire Cardie,et al.  Towards a General Rule for Identifying Deceptive Opinion Spam , 2014, ACL.

[44]  Mihai Surdeanu,et al.  The Stanford CoreNLP Natural Language Processing Toolkit , 2014, ACL.

[45]  Thomas L. Ngo-Ye,et al.  The influence of reviewer engagement characteristics on online review helpfulness: A text regression model , 2014, Decis. Support Syst..

[46]  Ravendra Ratan Singh Jandail.  A proposed Novel Approach for Sentiment Analysis and Opinion Mining , 2014 .

[47]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[48]  Arjun Mukherjee,et al.  Exploiting Burstiness in Reviews for Review Spammer Detection , 2021, ICWSM.

[49]  Abhinav Kumar,et al.  Spotting opinion spammers using behavioral footprints , 2013, KDD.

[50]  R. Rajasree,et al.  Sentiment analysis in twitter using machine learning techniques , 2013, 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT).

[51]  Christos Faloutsos,et al.  Opinion Fraud Detection in Online Reviews by Network Effects , 2013, ICWSM.

[52]  Arjun Mukherjee,et al.  What Yelp Fake Review Filter Might Be Doing? , 2013, ICWSM.

[53]  Henry Anaya-Sánchez,et al.  Retrieving Product Features and Opinions from Customer Reviews , 2013, IEEE Intelligent Systems.

[54]  Ronen Feldman,et al.  Techniques and applications for sentiment analysis , 2013, CACM.

[55]  Chihli Hung,et al.  Using Objective Words in SentiWordNet to Improve Word-of-Mouth Sentiment Classification , 2013, IEEE Intelligent Systems.

[56]  Björn W. Schuller,et al.  New Avenues in Opinion Mining and Sentiment Analysis , 2013, IEEE Intelligent Systems.

[57]  Dr. Y. S. Kumaraswamy,et al.  MINING USING DECISION TREE BASED FEATURE SELECTION THROUGH MANHATTAN HIERARCHICAL CLUSTER MEASURE , 2013 .

[58]  Yong Shi,et al.  The Role of Text Pre-processing in Sentiment Analysis , 2013, ITQM.

[59]  Veda C. Storey,et al.  Business Intelligence and Analytics: From Big Data to Big Impact , 2012, MIS Q..

[60]  P. Leeflang,et al.  Popularity of Brand Posts on Brand Fan Pages: An Investigation of the Effects of Social Media Marketing , 2012 .

[61]  Bing Liu,et al.  Sentiment Analysis and Opinion Mining , 2012, Synthesis Lectures on Human Language Technologies.

[62]  Jacob Goldenberg,et al.  Mine Your Own Business: Market-Structure Surveillance Through Text Mining , 2012, Mark. Sci..

[63]  Arjun Mukherjee,et al.  Spotting fake reviewer groups in consumer reviews , 2012, WWW.

[64]  Raymond Y. K. Lau,et al.  Text mining and probabilistic language modeling for online review spam detection , 2012, TMIS.

[65]  Quang-Thuy Ha,et al.  An Upgrading Feature-Based Opinion Mining Model on Vietnamese Product Reviews , 2011, AMT.

[66]  Yi Yang,et al.  Learning to Identify Review Spam , 2011, IJCAI.

[67]  Owen Rambow,et al.  Sentiment Analysis of Twitter Data , 2011 .

[68]  Christopher Potts,et al.  Learning Word Vectors for Sentiment Analysis , 2011, ACL.

[69]  Claire Cardie,et al.  Finding Deceptive Opinion Spam by Any Stretch of the Imagination , 2011, ACL.

[70]  Finn Årup Nielsen,et al.  A New ANEW: Evaluation of a Word List for Sentiment Analysis in Microblogs , 2011, #MSM.

[71]  Ee-Peng Lim,et al.  Detecting product review spammers using rating behaviors , 2010, CIKM.

[72]  Hao Yu,et al.  A Novel Product Features Categorize Method Based on Twice-Clustering , 2010, 2010 International Conference on Web Information Systems and Mining.

[73]  Kaifu Zhang,et al.  Contextual Advertising , 2011, Mark. Sci..

[74]  Alex S. L. Tsang,et al.  Is a “star” worth a thousand words?: The interplay between product‐review texts and rating valences , 2009 .

[75]  Kerstin Denecke,et al.  Using SentiWordNet for multilingual sentiment analysis , 2008, 2008 IEEE 24th International Conference on Data Engineering Workshop.

[76]  Bing Liu,et al.  Opinion spam and analysis , 2008, WSDM '08.

[77]  Lillian Lee,et al.  Opinion Mining and Sentiment Analysis , 2008, Found. Trends Inf. Retr..

[78]  Bing Liu,et al.  Analyzing and Detecting Review Spam , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

[79]  Seounmi Youn,et al.  Online Word-of-Mouth (or Mouse): An Exploration of Its Antecedents and Consequences , 2006, J. Comput. Mediat. Commun..

[80]  Hinrich Schütze,et al.  Introduction to information retrieval , 2008 .

[81]  Razvan C. Bunescu,et al.  Sentiment analyzer: extracting sentiments about a given topic using natural language processing techniques , 2003, Third IEEE International Conference on Data Mining.