[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content
Springer Nature Link
Log in

Applications and Resources for Social Media Sentiment Analysis: A Strategic Planning Case Study

  • Chapter
  • First Online:
Management Engineering in Emerging Economies

Part of the book series: Management and Industrial Engineering ((MINEN))

  • 213 Accesses

Abstract

The exponential increase in information on social media opens up new opportunities to unravel meaningful unstructured information. Since the recent coronavirus pandemic, the online activities have increased considerably. The application of sentiment analysis allows us to process and detect the polarity of opinions on a particular topic. However, this approach faces significant challenges due to the informal nature of user-generated posts. This scenario allows new analyses of companies, people, or organizations, valuable information on how their audience perceives them, and what aspects must be improved to make better decisions and find new opportunities. The results of this work show a comprehensive view, highlight the application of the technique, the datasets, and the challenges currently faced, and finally, a case study is presented where the significant knowledge in the comments is automatically oriented towards the key performance indicators and perspectives of a balanced scorecard, an original and relevant contribution which a group of opinion leaders of the company validated.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 119.99
Price includes VAT (United Kingdom)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
GBP 149.99
Price includes VAT (United Kingdom)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  • Abbasi-Moud Z, Vahdat-Nejad H, Sadri J (2020) Tourism recommendation system based on semantic clustering and sentiment analysis. Expert Syst Appl 167:114324

    Article  Google Scholar 

  • Afrizal AD, Rakhmawati NA, Tjahyanto A (2019) New filtering scheme based on term weighting to improve object based opinion mining on tourism product reviews. Procedia Comput Sci 161:805–812

    Article  Google Scholar 

  • Agnihotri R, Dingus R, Hu MY, Krush MT (2016) Social media: influencing customer satisfaction in B2B sales. Ind Mark Manage 53:172–180. https://doi.org/10.1016/j.indmarman.2015.09.003

    Article  Google Scholar 

  • Al Amrani A, Lazaar M, El Kadiri K (2018) Random forest and support vector machine based hybrid approach to SA–RF.pdf. Procedia Comput Sci 127:511–520

    Article  Google Scholar 

  • Aldayel HK, Azmi AM (2015) Arabic tweets sentiment analysis—a hybrid scheme. J Inf Sci 42(6):782–797

    Article  Google Scholar 

  • Aldiansyah MR, Sasongko PS (2019) Twitter sentiment analysis about public opinion on 4G smartfren network services using convolutional neural network. In: 2019—3rd international conference on informatics and computational sciences, pp 3–8

    Google Scholar 

  • Alfaro C, Cano-Montero J, Gómez J, Moguerza JM, Ortega F (2013) A multi-stage method for content classification and opinion mining on weblog comments. Ann Oper Res 236(1):197–213

    Article  Google Scholar 

  • Al-Natour S, Turetken O (2020) A comparative assessment of sentiment analysis and star ratings for consumer reviews. Int J Inf Manage 54(April):102132

    Article  Google Scholar 

  • Aloufi S, Alzamzami F, Hoda M, El Saddik A (2018) Soccer fans sentiment through the eye of big data: the UEFA champions league as a case study. In: Proceedings—IEEE 1st conference on multimedia information processing and retrieval, MIPR 2018, pp 244–250

    Google Scholar 

  • Annisa R, Surjandari I (2019) Opinion mining on mandalika hotel reviews using LDA. Procedia Comput Sci 161:739–746

    Google Scholar 

  • Ansari MZ, Aziz MB, Siddiqui MO, Mehra H, Singh KP (2020) Analysis of political sentiment orientations on twitter. Procedia Comput Sci 167:1821–1828. https://doi.org/10.1016/j.procs.2020.03.201

    Article  Google Scholar 

  • Appel O, Chiclana F, Carter J, Fujita H (2016) A hybrid approach to the sentiment analysis problem at the sentence level. Knowl-Based Syst 108:110–124. https://doi.org/10.1016/j.knosys.2016.05.040

    Article  Google Scholar 

  • Arias FM, Zambrano Núñez A, Guerra-Adames N-F, Vargas-Lombardo M (2022) Sentiment analysis of public social media as a tool for health-related topics. IEEE Access 10:74850–74872

    Article  Google Scholar 

  • Asani E, Vahdat-Nejad H, Sadri J (2021) Restaurant recommender system based on sentiment analysis. Machine Learn Appl 6:100114. https://doi.org/10.1016/j.mlwa.2021.100114

    Article  Google Scholar 

  • Azmi AM, Alzanin SM (2014) Aara’—a system for mining the polarity of Saudi public opinion through e-newspaper comments. J Inf Sci 40(3):398–410

    Article  Google Scholar 

  • Bae Y, Lee H (2012) Sentiment analysis of twitter audiences: measuring the positive or negative influence of popular twitterers. J Am Soc Inform Sci Technol 63(12):2251–2535

    Article  Google Scholar 

  • Bagheri A, Saraee M, De Jong F (2013) Care more about customers: unsupervised domain-independent aspect detection for sentiment analysis of customer reviews. Knowl-Based Syst 52(August):201–213

    Article  Google Scholar 

  • Barbosa RRL, Sánchez-Alonso S, Sicilia-Urban MA (2015) Evaluating hotels rating prediction based on sentiment analysis services. Aslib J Inf Manage 67(4):392–407

    Article  Google Scholar 

  • Bilici E, Saygın Y (2017) Why do people (not) like me?: mning opinion influencing factors from reviews. Expert Syst Appl 68:185–195

    Article  Google Scholar 

  • Burnap P, Williams ML, Sloan L, Rana O, Housley W, Edwards A, Knight V, Procter R, Voss A (2014) Tweeting the terror: modelling the social media reaction to the Woolwich terrorist attack. Soc Netw Anal Min 4(1):1–14

    Article  Google Scholar 

  • Cao D, Ji R, Lin D, Li S (2014) Visual sentiment topic model based microblog image SA. Multimed Tools Appl 75(15):8955–8968

    Google Scholar 

  • Ceron A, Curini L, Iacus SM, Porro G (2014) Every tweet counts? How sentiment analysis of social media can improve our knowledge of citizens’ political preferences with an application to Italy and France. New Media Soc 16(2):340–358. https://doi.org/10.1177/1461444813480466

    Article  Google Scholar 

  • Ceron A, Curini L, Iacus SM (2016) ISA: a fast, scalable and accurate algorithm for sentiment analysis of social media content. Inf Sci 367–368:105–124. https://doi.org/10.1016/j.ins.2016.05.052

    Article  Google Scholar 

  • Chakraborty K, Bhatia S, Bhattacharyya S, Platos J, Bag R, Hassanien AE (2020) Sentiment analysis of COVID-19 tweets by deep learning classifiers—a study to show how popularity is affecting accuracy in social media. Appl Soft Comput J 1–13

    Google Scholar 

  • Chakravarthi BR, Priyadharshini R, Muralidaran V et al (2022) DravidianCodeMix: sentiment analysis and offensive language identification dataset for Dravidian languages in code-mixed text. Lang Resour Eval 56:765–806. https://doi.org/10.1007/s10579-022-09583-7

    Article  Google Scholar 

  • Che S, Zhu W, Li X (2020) Anticipating corporate financial performance from CEO letters utilizing sentiment analysis. Math Problems Eng 2020

    Google Scholar 

  • Chen J, Huang DP, Hu S, Liu Y, Cai Y, Min H (2014) An opinion mining framework for Cantonese reviews. J Ambient Intell Humaniz Comput 6(5):541–547. https://doi.org/10.1007/s12652-014-0237-8

    Article  Google Scholar 

  • Chen W, Cai Y, Lai K, Xie H (2016) A topic-based sentiment analysis model to predict stock market price movement using Weibo mood. Web Intell 14(4):287–300.https://doi.org/10.3233/WEB-160345

  • Chiarello F, Bonaccorsi A, Fantoni G (2020) Technical sentiment analysis. Measuring advantages and drawbacks of new products using social media. Comput Indus 123:103299

    Google Scholar 

  • Chih-Fong T, Chen K, Hu YH, Chen WK (2020) Improving text summarization of online hotel reviews with review helpfulness and sentiment. Tourism Manage 80(February 2019):1–13

    Google Scholar 

  • Chiu C, Chiu NH, Sung RJ, Hsieh PY (2013) Opinion mining of hotel customer-generated contents in Chinese weblogs. Curr Issue Tour 18(5):477–495. https://doi.org/10.1080/13683500.2013.841656

    Article  Google Scholar 

  • Choi Y (2019) Finding “just right” books for children: analyzing sentiments in online book reviews. Electronic Library 37(3):563–576

    Article  Google Scholar 

  • Chung W, Zeng D (2016) Social-media-based public policy informatics: sentiment and network analyses of US immigration and border security. J Am Soc Inform Sci Technol 64:1587–1606. https://doi.org/10.1002/asi.23449

    Article  Google Scholar 

  • D’Andrea E, Ducange P, Bechini A, Renda A, Marcelloni F (2019) Monitoring the public opinion about the vaccination topic from tweets analysis. Expert Syst Appl 116:209–226

    Article  Google Scholar 

  • D’Avanzo E, Pilato G (2015) Mining social network users opinions’ to aid buyers’ shopping decisions. Comp Human Behavior 51:1284–1294

    Article  Google Scholar 

  • Derakhshan A, Beigy H (2019) Sentiment analysis on stock social media for stock price movement prediction. Eng Appl Artif Intell 85:569–578. https://doi.org/10.1016/j.engappai.2019.07.002

    Article  Google Scholar 

  • Deshmukh JS, Tripathy AK (2018) Entropy based classifier for cross-domain opinion mining. Appl Comput Inform 14(1):55–64

    Article  Google Scholar 

  • Dong R, O’Mahony MP, Schaal M, McCarthy K, Smyth B (2015) Combining similarity and sentiment in opinion mining for product recommendation. J Intell Inf Syst 46(2):285–312

    Article  Google Scholar 

  • Dragoni M, Petrucci G (2017) A fuzzy-based strategy for multi-domain sentiment analysis. Int J Approximate Reason 93:59–73

    Article  MathSciNet  Google Scholar 

  • Driscoll B (2015) Sentiment analysis and the literary festival audience. Continuum J Media & Cultural Stud 29(6):861–873

    Article  Google Scholar 

  • Eliacik AB, Erdogan N (2017) Influential user weighted sentiment analysis on topic based microblogging community. Expert Syst Appl 92:403–418. https://doi.org/10.1016/j.eswa.2017.10.006

    Article  Google Scholar 

  • Feuerriegel S, Gordon J (2018) Long-term stock index forecasting based on TM of regul disclosures. Decis Support Syst 112:88–97

    Article  Google Scholar 

  • Ficcadenti V, Cerqueti R, Ausloos M (2019) A joint text mining-rank size investigation of the rhetoric structures of the US Presidents’ speeches. Expert Syst Appl 123:127–142

    Article  Google Scholar 

  • Gabarron E, Dorronzoro E, Rivera-Romero O, Wynn R (2019) Diabetes on twitter: a sentiment analysis. J Diabetes Sci Technol 13(3):439–444. https://doi.org/10.1177/1932296818811679

    Article  Google Scholar 

  • Garcia K, Berton L (2020) Topic detection and sentiment analysis in Twitter content related to COVID-19 from Brazil and the USA. Appl Soft Comput 101(March):107057. https://doi.org/10.1016/j.asoc.2020.107057

    Article  Google Scholar 

  • Ghahramani M, Galle JN, Ratti C, Pilla F (2021) Tales of a city: sentiment analysis of urban green space in Dublin, Cities 119:103395

    Google Scholar 

  • Ghiassi M, Lee S (2018) A domain transferable lexicon set for Twitter sentiment analysis using a supervised machine learning approach. Expert Syst Appl 106:197–216. https://doi.org/10.1016/j.eswa.2018.04.006

    Article  Google Scholar 

  • Giachanou A, Crestani F (2016) Like it or not: a survey of twitter sentiment analysis methods. ACM Comput Surv 49(2)

    Google Scholar 

  • Gitto S, Mancuso P (2017) Improving airport services using sentiment analysis of the websites. Tour Manage Perspect 22:132–136. https://doi.org/10.1016/j.tmp.2017.03.008

    Article  Google Scholar 

  • Gopalakrishnan V, Ramaswamy C (2017) Patient opinion mining to analyze drugs satisfaction using supervised learning. J Appl Res Technol 15(4):311–319. https://doi.org/10.1016/j.jart.2017.02.005

    Article  Google Scholar 

  • Guo Y, Barnes SJ, Jia Q (2017) Mining meaning from online ratings and reviews: tourist satisfaction analysis using latent dirichlet allocation. Tour Manage 59:467–483

    Article  Google Scholar 

  • Haeng-Jin J, Sim J, Lee Y, Kwon O (2013) Deep sentiment analysis: mining the causality between personality-value- attitude for analyzing business ads in social media. Expert Syst Appl 40(18):7492–7503

    Article  Google Scholar 

  • Hai Z, Chang K, Kim JJ, Yang CC (2014) Identifying features in opinion mining via intrinsic and extrinsic domain relevance. IEEE Trans Knowl Data Eng 26(3):623–634

    Article  Google Scholar 

  • Haryanto B, Ruldeviyani Y, Rohman F, Julius Dimas TN, Magdalena R, Muhamad Yasil F (2019) Facebook analysis of community sentiment on 2019 Indonesian presidential candidates from Facebook opinion data. Procedia Comput Sci 161:715–722

    Article  Google Scholar 

  • Hossain MS, Uddin MK, Hossain MK, Rahman MF (2022) User sentiment analysis and review rating prediction for the blended learning platform app. In: Trajkovski G, Demeter M, Hayes H (eds) Applying data science and learning analytics throughout a learner’s lifespan. IGI Global, pp 113–132. https://doi.org/10.4018/978-1-7998-9644-9.ch006

  • Hutto CJ, Gilbert E (2014) VADER: a parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the 8th international conference on weblogs social media, ICWSM 2014, pp 216–225

    Google Scholar 

  • Ireland R, Liu A (2018) Application of data analytics for product design: Sentiment analysis of online product reviews. CIRP J Manuf Sci Technol 23:128–144

    Article  Google Scholar 

  • Ji P, Zhang HY, Wang JQ (2019) A fuzzy decision support model with sentiment analysis for items comparison in e-Commerce: the case study of http://PConline.com. IEEE Trans Syst, Man, Cybern: Syst 49(10):1993–2004. https://doi.org/10.1109/TSMC.2018.2875163

  • Jiang H, Lin P, Qiang M (2016) Public-opinion sentiment analysis for large hydro projects. J Constr Eng Manage 142(2):05015013

    Article  Google Scholar 

  • Jiang H, Kwong CK, Okudan Kremer GE, Park WY (2019) Dynamic modelling of customer preferences for product design using DENFIS and opinion mining. Adv Eng Inform 42(October 2018):1–12

    Google Scholar 

  • Jindal N, Liu B (2006) Identifying comparative sentences in text documents. In: Proceedings of the twenty-ninth annual international ACM SIGIR conference on research and development in information retrieval, pp 244–251

    Google Scholar 

  • Kandasamy I, Vasantha WB, Obbineni JM, Smarandache F (2019) Sentiment analysis of tweets using refined neutrosophic sets. Comput Ind 115:1–11

    Google Scholar 

  • Kang Y, Wang Y, Zhang D, Zhou L (2017) The public’s opinions on a new school meals policy for childhood obesity prevention in the US: a social media analytics approach. Int J Med Inform 103:83–88

    Article  Google Scholar 

  • Kauffmann E, Peral J, Gil D, Ferrández A, Sellers R, Mora H (2019) A framework for big data analytics in commercial social networks: a case study on sentiment analysis and fake review detection for marketing decision-making. Indus Market Manage 1–15

    Google Scholar 

  • Kazmaier J, van Vuuren JH (2020) A generic framework for sentiment analysis: leveraging opinion-bearing data to inform decision making. Decis Support Syst 135:1–56. https://doi.org/10.1016/j.dss.2020.113304

    Article  Google Scholar 

  • Kim H-J, Jeong YK, Kim Y, Kang KY, Song M (2016) Topic-based content and sentiment analysis of Ebola virus on twitter and in the news. J Inf Sci 1–19

    Google Scholar 

  • Khan FH, Bashir S, Qamar U (2014) TOM: twitter opinion mining framework using hybrid classification scheme. Dec Supp Syst 57(1):245–257

    Article  Google Scholar 

  • Kim D, Kim D, Hwang E, Choi HG (2013) A user opinion and metadata mining scheme for predicting box office performance of movies in the social network environment. New Rev Hypermedia Multimedia 19(3–4):259–272

    Google Scholar 

  • Korkontzelos I, Nikfarjam A, Shardlow M, Sarker A, Ananiadou S, Gonzalez GH (2016) Analysis of the effect of SA on extracting adverse drug reactions from tweets. J Biomed Inform 62:148–158

    Article  Google Scholar 

  • Kraaijeveld O, De Smedt J (2020) The predictive power of public twitter sentiment for forecasting cryptocurrency prices. J Int Finan Markets Inst Money 65:1–22

    Article  Google Scholar 

  • Kušen E, Strembeck M (2018) Politics, sentiments, and misinformation: an analysis of the twitter discussion on the 2016 Austrian presidential elections. Online Soc Netw Media 5:37–50

    Article  Google Scholar 

  • Lazhar F, Yamina TG (2016) Mining explicit and implicit opinions from reviews. Int J Data Mining, Modelling, Manage 8(1):75–92

    Article  Google Scholar 

  • Lin H-C, Wang T-H, Lin G-C, Cheng S-C, Chen H-R, Huang Y-M (2020) Applying sentiment analysis to automatically classify consumer comments concerning marketing 4Cs aspects. Appl Soft Comput J 97:106755. https://doi.org/10.1016/j.asoc.2020.106755

  • Li H, Cui J, Shen B, Ma J (2016) An intelligent movie recommendation system through group-level sentiment analysis in microblogs. Neurocomputing 210:164–173

    Google Scholar 

  • Li W, Zhu L, Shi Y, Guo K, Cambria E (2020) User reviews: sentiment analysis using lexicon integrated two-channel CNN–LSTM​ family models. Appl Soft Comput J 94:106435

    Google Scholar 

  • Li X, Wu P, Wang W (2020) Incorporating stock prices and news sentiments for stock market prediction: a case of Hong Kong. Inf Process Manage 1–19. https://doi.org/10.1016/j.ipm.2020.102212

  • Li J, Meesad P (2016) Combining sentiment analysis with socialization bias in social networks for stock market trend prediction. Int J Comput Intell Appl 15(1):1–16

    Google Scholar 

  • Liang D, Dai Z, Wang M, Li J (2020) Web celebrity shop assessment and improvement based on online review with probabilistic linguistic term sets using sentiment analysis and fuzzy cognitive map. Fuzzy Optim Decis Making 19(4):561–586. https://doi.org/10.1007/s10700-020-09327-8

    Article  MathSciNet  Google Scholar 

  • Liang TP, Li X, Yang CT, Wang M (2015) What in consumer reviews affects the sales of mobile apps: a multifacet sentiment analysis approach. Int J Electron Commerce 20(2):236–260

    Google Scholar 

  • Lin Y, Wang X, Li Y, Zhou A (2015) Integrating the optimal classifier set for sentiment analysis. Soc Netw Anal Min 5(1):1–13

    Article  Google Scholar 

  • Liu, & Lei. (2018) The appeal to political sentiment: an analysis of Donald Trump’s and Hillary Clinton’s speech themes and discourse strategies in the 2016 US presidential election. Discourse, Context and Media 25:143–152

    Article  Google Scholar 

  • Liu B (2012) Sentiment analysis and opinion mining (G. H. (University of Toronto) (ed.)). Morgan & Claypool

    Google Scholar 

  • Lyu YW, Chun-Chung Chow J, Ji-Jen H (2020) Exploring public attitudes of child abuse in mainland China: a sentiment analysis of China’s social media Weibo. Child Youth Serv Rev 116(April):105250

    Article  Google Scholar 

  • Mahmud MS, Jaman Bonny A, Saha U, Jahan M, Tuna ZF, Al Marouf A (2022) Sentiment analysis from user-generated reviews of ride-sharing mobile applications. In: 2022 6th international conference on computing methodologies and communication (ICCMC). Erode, India, pp 738–744

    Google Scholar 

  • Malekpour Koupaei D, Song T, Cetin KS, Im J (2020) An assessment of opinions and perceptions of smart thermostats using aspect-based sentiment analysis of online reviews. Build Environ 170(Dec):106603

    Google Scholar 

  • Marrese-Taylor E, Velásquez JD, Bravo-Marquez F (2014) A novel deterministic approach for aspect-based opinion mining in tourism products reviews. Expert Syst Appl 41(17):7764–7775

    Article  Google Scholar 

  • Mei Y, Tu Y, Xie K, Ye Y, Shen W (2019) Internet public opinion risk grading under emergency event based on AHPSort II-DEMATEL. Sustainability (switzerland) 11(16):2–16

    Google Scholar 

  • Moraes R, Valiati JF, Gavião Neto WP (2013) Document-level sentiment classification: an empirical comparison between SVM and ANN. Expert Syst Appl 40(2):621–633

    Article  Google Scholar 

  • Mostafa MM (2018) Clustering halal food consumers: a twitter sentiment analysis. Int J Mark Res 61(3):320–337

    Article  Google Scholar 

  • Mućko P (2021) Sentiment analysis of CSR disclosures in annual reports of EU companies. Procedia Comput Sci 192:3351–3359

    Article  Google Scholar 

  • Muhammad A, Wiratunga N, Lothian R (2016) Contextual sentiment analysis for social media genres. Knowl-Based Syst 108:92–101

    Article  Google Scholar 

  • Nassirtoussi AK, Aghabozorgi S, Ying WT, Ngo DCL (2014) Text mining for market prediction: a systematic review. Expert Syst Appl 41(16):7653–7670. https://doi.org/10.1016/j.eswa.2014.06.009

  • Neppalli VK, Caragea C, Squicciarini A, Tapia A, Stehle S (2017) Sentiment analysis during Hurricane Sandy in emergency response. Int J Disaster Risk Reduction 21(May 2016):213–222

    Google Scholar 

  • Nguyen TH, Shirai K, Velcin J (2015) Sentiment analysis on social media for stock movement prediction. Expert Syst Appl 42(24):9603–9611. https://doi.org/10.1016/j.eswa.2015.07.052

    Article  Google Scholar 

  • Oke A, Osobajo O, Obi L, Omotayo T (2020) Rethinking and optimising post-consumer packaging waste: a sentiment analysis of consumers’ perceptions towards the introduction of a deposit refund scheme in Scotland. Waste Manage 118(2020):463–470

    Article  Google Scholar 

  • Ouyang Y, Zhang HW, Li X, Xiong Z (2020) MOOC opinion mining based on attention alignment. Inf Discov Deliv 1–9

    Google Scholar 

  • Öztürk N, Ayvaz S (2018) Sentiment analysis on twitter: a text mining approach to the Syrian refugee crisis. Telematics Inform 35(1):136–147

    Article  Google Scholar 

  • Pai PF, Liu CH (2018) Predicting vehicle sales by sentiment analysis of twitter data and stock market values. IEEE Access 6:57655–57662

    Article  Google Scholar 

  • Paltoglou G, Thelwall M (2012) Twitter, MySpace, Digg: unsupervised sentiment analysis in social media. ACM Trans Intell Syst Technol 3(4). https://doi.org/10.1145/2337542.2337551

  • Parama Fadli Kurnia S (2018) Business intelligence model to analyze social media information. Procedia Comput Sci 135:5–14

    Google Scholar 

  • Paramanik RN, Singhal V (2020) Sentiment analysis of Indian stock market volatility. Procedia Comput Sci 176:330–338

    Article  Google Scholar 

  • Pathak AR, Pandey M, Rautaray S (2021) Topic-level sentiment analysis of social media data using deep learning. Appl Soft Comput 108:107440. https://doi.org/10.1016/j.asoc.2021.107440

  • Pavaloaia VD, Teodor EM, Fotache D, Danileţ M (2019) Opinion mining on social media data: sentiment analysis of user preferences. Sustainability (switzerland) 11(16):1–21. https://doi.org/10.3390/su11164459

    Article  Google Scholar 

  • Petz G, Karpowicz M, Fürschuß H, Auinger A, Stříteský V, Holzinger A (2014) Computational approaches for mining user’s opinions on the Web 2.0. Inf Process Manage 50(6):899–908

    Google Scholar 

  • Philander K, Zhong YY (2016) Twitter sentiment analysis: capturing sentiment from integrated resort tweets. Int J Hosp Manage 55:16–24. https://doi.org/10.1016/j.ijhm.2016.02.001

    Article  Google Scholar 

  • Piryani R, Madhavi D, Singh VK (2017) Analytical mapping of opinion mining and sentiment analysis research during 2000–2015. Inf Process Manage 53(1):122–150

    Article  Google Scholar 

  • Ramanathan U, Subramanian N, Parrott G (2017) Role of social media in retail network operations and marketing to enhance customer satisfaction. Int J Oper Product Manage 37(1):105–123

    Article  Google Scholar 

  • Rathan M, Hulipalled VR, Venugopal KR, Patnaik LM (2018) Consumer insight mining: aspect based twitter opinion mining of mobile phone reviews. Appl Soft Comput J 68:765–773

    Article  Google Scholar 

  • Ravi K, Ravi V, Prasad PSRK (2017) Fuzzy formal concept analysis based opinion mining for CRM in financial services. Appl Soft Comput J 60:786–807. https://doi.org/10.1016/j.asoc.2017.05.028

  • Ren R, Wu DD, Wu DD (2019) Forecasting stock market movement direction using sentiment analysis and support vector machine. IEEE Syst J 13(1):760–770. https://doi.org/10.1109/JSYST.2018.2794462

    Article  Google Scholar 

  • Rodrigues RG, das Dores RM, Camilo-Junior CG, Rosa TC (2015) SentiHealth-Cancer: a sentiment analysis tool to help detecting mood of patients in online social networks. Int J Med Inform 85(1):80–95. https://doi.org/10.1016/j.ijmedinf.2015.09.007

  • Saif H, He Y, Fernandez M, Alani H (2016) Contextual semantics for sentiment analysis of twitter. Inform Process Manage 52(1):5–19

    Article  Google Scholar 

  • Salas-Zárate M, del P, Estanislao L-L, Valencia-García R, Aussenac-Gilles N, Almela Á, Alor-Hernández G (2014) A study on LIWC categories for opinion mining in Spanish reviews. J Intell Mater Syst Struct 26(5):599–613. https://doi.org/10.1177/0165551514547842

  • Sandhu M, Vinson CD, Mago VK, Giabbanelli PJ (2019) From associations to sarcasm: mining the shift of opinions regarding the supreme court on twitter. Online Soc Netw Media 14:1–11

    Google Scholar 

  • Sarker IH (2021) Deep learning: a comprehensive overview on techniques, taxonomy, applica and res directions. SN Comput Sci 2:420

    Article  Google Scholar 

  • Schumaker RP, Jarmoszko AT, Labedz CS (2016) Predicting wins and spread in the premier league using a sentiment analysis of twitter. Decis Support Syst 88:76–84. https://doi.org/10.1016/j.dss.2016.05.010

    Article  Google Scholar 

  • Schumaker RP, Labedz CS, Jarmoszko AT, Brown LL (2017) Prediction from regional angst—a study of NFL sentiment in Twitter using technical stock market charting. Decis Support Syst 98:80–88

    Article  Google Scholar 

  • Severyn A, Moschitti A, Uryupina O, Plank B, Filippova K (2016) Multi-lingual opinion mining on YouTube. Inf Process Manage 52(1):46–60. https://doi.org/10.1016/j.ipm.2015.03.002

    Article  Google Scholar 

  • Sharma A, Shekhar H (2020) Intelligent learning based opinion mining model for governmental decision making. Procedia Comput Sci 173(2019):216–224

    Article  Google Scholar 

  • Sharma A, Dey S (2012) A comparative study of feature selection and machine learning techniques for sentiment analysis. RACS’12, pp 1–7

    Google Scholar 

  • Shayaa S, Jaafar NI, Bahri S, Sulaiman A, Seuk Wai P, Wai Chung Y, Piprani AZ, Al-Garadi MA (2018) Sentiment analysis of big data: methods, applications, and open challenges. IEEE Access 6:37807–37827

    Article  Google Scholar 

  • Shi W, Wang H, He S (2014) EOSentiMiner: an opinion-aware system based on emotion ontology for sentiment analysis of Chinese online reviews. J Exp Theoret Artif Intell 27(4):423–448

    Article  Google Scholar 

  • Shivaprasad TK, Shetty J (2017) SA of product reviews: a review. In: Proceedings of the international conference on inventive communication and computational technologies, pp 298–303

    Google Scholar 

  • Shuang K, Zhang Z, Guo H, Loo J (2018) A sentiment infor collector–extractor architecture based neural network for SA. Inf Sci 467:549–558

    Google Scholar 

  • Siganos A, Vagenas-Nanos E, Verwijmeren P (2017) Divergence of sentiment and stock market trading. J Bank Finance 78:130–141. https://doi.org/10.1016/j.jbankfin.2017.02.005

    Article  Google Scholar 

  • Silva W, Santana Á, Lobato F, Pinheiro M (2017) A methodology for community detection in twitter. In: Proceedings—2017 IEEE/WIC/ACM international conference on web intelligence, WI 2017, pp 1006–1009

    Google Scholar 

  • Singh A, Shukla N, Mishra N (2017) Social media data analytics to improve supply chain management in food industries. Transport Res Part E: Logistics Transport Rev 114:398–415

    Article  Google Scholar 

  • Singh M, Jakhar AK, Pandey S (2021) SS on the impact of coronavirus in social life using BERT model. Soc Netw Anal Min 11:33

    Google Scholar 

  • Son LH, Kumar A, Sangwan SR, Arora A, Nayyar A, Abdel-Basset M (2019) Sarcasm detection using soft attention-based bidirectional long short-term memory model with convolution network. IEEE Access 7:23319–23328

    Article  Google Scholar 

  • Srinivas S, Rajendran S (2018) Topic-based knowledge mining of online reviews for strategic planning. Comp Indust Eng 128:974–984

    Article  Google Scholar 

  • Stavrianou A, Brun C (2013) Expert recommendations based on opinion mining of user-generated product reviews. Comput Intell 31(1):165–183

    Article  MathSciNet  Google Scholar 

  • Sun S, Luo C, Chen J (2017) A review of natural language processing techniques for opinion mining systems. Inf Fusion 36:10–25

    Google Scholar 

  • Sun Q, Niu J, Yao Z, Yan H (2019) Exploring eWOM in online customer reviews: sentiment analysis at a fine-grained level. Eng Appl Artif Intell 81(Feb):68–78

    Google Scholar 

  • Swathi T, Kasiviswanath N, Rao AA (2022) An optimal deep learning-based LSTM for stock price prediction using twitter sentiment analysis. Appl Intell 52:13675–13688. https://doi.org/10.1007/s10489-022-03175-2

    Article  Google Scholar 

  • Tan S, Wu Q (2011) A random walk algorithm for automatic construction of domain-oriented sentiment lexicon. Expert Syst Appl 38(10):12094–12100

    Article  Google Scholar 

  • Uren V, Wright D, Scott J, He Y, Saif H (2016) Article information. Int J Energy Sector Manage 1–14

    Google Scholar 

  • Valdivia A, Hrabova E, Chaturvedi I, Luzón MV, Troiano L, Cambria E, Herrera F (2019) Inconsistencies on tripadvisor reviews: a unified index between users and sentiment analysis methods. Neurocomputing 353:3–16

    Article  Google Scholar 

  • Vashishtha S, Susan S (2019) Fuzzy rule based unsupervised sentiment analysis from social media posts. Expert Syst Appl 138

    Google Scholar 

  • Vásquez Rojas C, Roldán Reyes E, Aguirre y Hernández F, Cortés Robles G (2018) Integration of a text mining approach in the strategic planning process of small and medium-sized enterprises. Indus Manage Data Syst 118(4):745–764. https://doi.org/10.1108/IMDS-01-2017-0029

  • Vilares D, Alonso MA, Gómez-Rodríguez C (2013) A syntactic approach for opinion mining on Spanish reviews. Nat Lang Eng 21(1):139–163

    Article  Google Scholar 

  • Vuleta B (2020) How much data is produced every day? https://seedscientific.com/how-much-data-is-created-every-day/

  • Wang T, Lu K, Chow KP, Zhu Q (2020) COVID-19 sensing: negative SA on social media in China via BERT model. IEEE Access 8:1–8

    Google Scholar 

  • Woolley S (2016) Automating power: social bot interference in global politics. First Monday 21(4):1–12

    Google Scholar 

  • Yadav A, Vishwakarma DK (2020) Sentiment analysis using deep learning architectures: a review. Artif Intell Rev 53:4335–4385

    Article  Google Scholar 

  • Yoon B, Jeong Y, Lee K, Lee S (2020) A systematic approach to prioritizing R&D projects based on customer-perceived value using opinion mining. Technovation 98(June):102164

    Article  Google Scholar 

  • Yu Y, Wang X (2015) World Cup 2014 in the twitter world: a big data analysis in US sports fans’ tweets. Comp in Hum Beh 48:392–400

    Article  Google Scholar 

  • Zadeh LA (2015) Fuzzy logic—a personal perspective. Fuzzy Sets Syst 281:4–20

    Article  MathSciNet  Google Scholar 

  • Zaidi A, Oussalah M (2018) Forecasting weekly crude oil using twitter sentiment of US foreign policy and oil companies data. In: Proceedings—2018 IEEE 19th international conference on information reuse and integration for data science, IRI 2018, pp 201–208

    Google Scholar 

  • Zavattaro SM, French PE, Mohanty SD (2015) A sentiment analysis of US local government tweets: the connection between tone and citizen involvement. Gov Inf Q 32(3):333–341

    Article  Google Scholar 

  • Zengin Alp Z, Gündüz Öğüdücü Ş (2018) Identifying topical influencers on twitter based on user behavior and network topology. Knowl-Based Syst 141:211–221

    Article  Google Scholar 

  • Zhang H, Sekhari A, Ouzrout Y, Bouras A (2016) Jointly identifying opinion mining elements and fuzzy measurement of opinion intensity to analyze product features. Eng Appl Artif Intell 47(2012):122–139. https://doi.org/10.1016/j.engappai.2015.06.007

    Article  Google Scholar 

  • Zhang Z, Ye Q, Zhang Z, Li Y (2011) Sentiment classification of Internet restaurant reviews written in Cantonese. Expert Syst Appl 38(6):7674–7682.https://doi.org/10.1016/j.eswa.2010.12.147

  • Zhao Z, Zhu H, Xue Z, Liu Z, Tian J, Chua MCH, Liu M (2019) An image-text consistency driven multimodal sentiment analysis approach for social media. Inf Process Manage 56(6):1–13

    Article  Google Scholar 

  • Zhao Y, Qin B, Liu T, Tang D (2014) Social sentiment sensor: a visualization system for topic detection and topic sentiment analysis on microblog. Multimedia Tools Appl 75(15):8843–8860

    Google Scholar 

  • Zhou Q, Xia R, Zhang C (2016) Online shopping behavior study based on multi-granularity opinion mining: China versus America. Cogn Comput 8(4):587–602. https://doi.org/10.1007/s12559-016-9384-x

    Article  Google Scholar 

  • Zhu B, Zheng X, Liu H, Li J, Wang P (2020) Analysis of spatiotemporal characteristics of big data on social media sentiment with COVID-19 epidemic topics. Chaos, Solitons Fractals 140:1–10. https://doi.org/10.1016/j.chaos.2020.110123

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Division of Research and Postgraduate Studies, Tecnológico Nacional de México TecNM, Mexico City, Mexico

    José Roberto Grande-Ramírez, Alberto A. Aguilar-Lasserre & Guillermo Alfredo Arrioja-Carrera

  2. Instituto Tecnológico de Orizaba, Orizaba, Veracruz, Mexico

    José Roberto Grande-Ramírez, Alberto A. Aguilar-Lasserre & Guillermo Alfredo Arrioja-Carrera

  3. Department of Industrial Maintenance, Universidad Tecnológica del Centro de Veracruz. Cuitláhuac, Veracruz, Mexico

    José Ernesto Domínguez-Herrera

Authors
  1. José Roberto Grande-Ramírez
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alberto A. Aguilar-Lasserre
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Guillermo Alfredo Arrioja-Carrera
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. José Ernesto Domínguez-Herrera
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to José Roberto Grande-Ramírez .

Editor information

Editors and Affiliations

  1. Av. Instituto Tecnologico, Institute of Technology of Orizaba, Orizaba, Veracruz, Mexico

    Guillermo Cortés-Robles

  2. Institute of Technology of Orizaba, Orizaba, Veracruz, Mexico

    Eduardo Roldán-Reyes

  3. Institute of Technology of Orizaba, Orizaba, Veracruz, Mexico

    Fernando Aguirre-y-Hernández

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Grande-Ramírez, J.R., Aguilar-Lasserre, A.A., Arrioja-Carrera, G.A., Domínguez-Herrera, J.E. (2024). Applications and Resources for Social Media Sentiment Analysis: A Strategic Planning Case Study. In: Cortés-Robles, G., Roldán-Reyes, E., Aguirre-y-Hernández, F. (eds) Management Engineering in Emerging Economies. Management and Industrial Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-54485-9_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-54485-9_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-54484-2

  • Online ISBN: 978-3-031-54485-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics