A new big data approach for topic classification and sentiment analysis of Twitter data

Evolutionary Intelligence - Tập 15 Số 2 - Trang 877-887 - 2022
Anisha P Rodrigues1, Niranjan N. Chiplunkar1
1Department of Computer Science and Engineering, NMAM Institute of Technology, Nitte, Udupi District, India

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Tài liệu tham khảo

Barnaghi P, Ghaffari P, Breslin JG (2016) Opinion mining and sentiment polarity on Twitter and correlation between events and sentiment. In: Proceedings of IEEE second international conference on big data computing and application, Oxford

Liu B (2010) Sentiment analysis and subjectivity. In: Indurkhya N, Damerau FJ (eds) Handbook of natural language processing, 2nd edn. Taylor and Francis Group, Boca Raton

Hadoop. http://hadoop.apache.org/. Accessed 12 Feb 2018

Flume. http://flume.apache.org/. Accessed 12 Feb 2018

Hive. http://hive.apache.org/. Accessed 12 Feb 2018

Kumar S, Morstatter F, Liu H (2013) Twitter data analytics. Springer, Berlin

Bouazizi M, Ohtsuki T (2017) A pattern-based approach for multi-class sentiment analysis in Twitter. IEEE Access 5:20617–20639

Jianqiang Z, Xiaolin G, Xuejun Z (2018) Deep convolution neural networks for Twitter sentiment analysis. IEEE Access 6:23253–23260

Pai P-F, Liu C-H (2018) Predicting vehicle sales by sentiment analysis of Twitter data and stock market values. IEEE Access 6:57655–57662

Gaikar DD, Marakarkandy B, Dasgupta C (2015) Using Twitter data to predict the performance of Bollywood movies. Indus Manag Data Syst 115(9):1604–1621

Tugores A, Colet P (2014) Mining online social networks with python to study urban mobility. arXiv preprint arXiv:1404.6966

Bifet A, Frank E (2010) Sentiment knowledge discovery in Twitter streaming data. In: Proceedings of 13th international conference on discovery science, lecture notes in computer science, vol 6332, pp 1–15. Springer, Berlin

Ha I, Back B, Ahn B (2015) MapReduce functions to analyze sentiment information from social big data. Int J Distrib Sensor Netw 2015:1–11

Tufekci Z (2014) Big questions for social media big data: representativeness, validity and other methodological pitfalls. arXiv preprint arXiv:1403.7400

Shirahatti AP, Patil N, Kubasad D, Mujawar A (2015) Sentiment analysis on Twitter data using Hadoop. Int J Emerg Technol Comput Sci Electron (IJETCSE) 14(2):1

Kim S-M, Hovy E (2004) Determining the sentiment of opinions. In: Proceedings of the 20th international conference on computational linguistics (COLING’04), Geneva, Switzerland

Hu M, Liu B (2004) Mining and summarizing customer reviews. In: Proceedings of 10th ACM SIGKDD international conference on knowledge discovery and data mining (KDD’04), Seattle, WA

Hatzivassiloglou V, McKeown K (1997) Predicting the semantic orientation of adjectives. In: Proceedings of 35th annual meeting of the association for computational linguistics and eighth conference of the European chapter of the association for computational linguistics, Madrid, pp 174–181

Read J, Carroll J (2009) Weakly supervised techniques for domain independent sentiment classification. In: Proceeding of the 1st international CIKM workshop on topic-sentiment analysis for mass opinion, Hong Kong, pp 45–52

Xu K, Liao SS, Li J, Song Y (2011) Mining comparative opinions from customer reviews for competitive intelligence. Decis Support Syst 50(4):743–754

Jiao J, Zhou Y (2011) Sentiment polarity analysis based multi-dictionary. Phys Proc 22:590–596

Qiu G, He X, Zhang F, Shi Y, Jiajun B, Chen C (2010) DASA: dissatisfaction-oriented advertising based on sentiment analysis. Expert Syst Appl 37(9):6182–6191

Chen CC, Tseng Y-D (2011) Quality evaluation of product reviews using an information quality framework. Decis Support Syst 50(4):755–768

Pang B, Lee L (2008) Using very simple statistics for review search: an exploration. In: Proceedings of the international conference on computational linguistics (COLING), pp 73–76

Dave K, Lawrence S, Pennock DM (2003) Mining the peanut gallery: opinion extraction and semantic classification of product reviews.  In: Proceedings of 12th international conference on the world wide web. ACM Press, pp. 519–528

Parikh R, Movassate M (2009) Sentiment analysis of user-generated twitter updates using various classification techniques. CS224N final report

Chauhan M, Yadav D (2015) Sentimental analysis of product based reviews using machine learning approaches. J Netw Commun Emerg Technol (JNCET) 5(2):19–25

Pak A, Paroubek P (2010) Twitter as a corpus for sentiment analysis and opinion mining. In: Proceedings of the seventh conference on international language resources and evaluation, pp 1320–1326

Selvan LGS, Moh T-S (2015) A framework for fast-feedback opinion mining on Twitter data streams. In: Proceedings of IEEE international conference on collaboration technologies and systems (CTS), Atlanta, GA, pp 314–318

Bhardwaj A, Vanraj A, Kumar A, Narayan Y, Kumar P (2015) Big data emerging technologies: a case study with analyzing Twitter data using apache hive. In: Proceedings of the 2nd international conference on recent advances in engineering and computational sciences (RAECS), Chandigarh, India, pp 1–6

Barskar A, Phulre A (2017) Opinion mining of Twitter data using Hadoop and apache pig. Int J Comput Appl 158(9):1–6

Prabhat A, Khullar V (2017) Sentiment classification on big data using Naïve Bayes and logistic regression. In: Proceedings of the international conference on computer communication and informatics (ICCCI), Coimbatore, India, pp 1–5

Shang S, Shi M, Shang W, Hong Z (2015) Research on public opinion based on Big Data. In: Proceedings of 14th international conference on computer and information science (ICIS), Las Vegas, NV, pp 559–562

Dean J, Ghemawat S (2004) MapReduce: simplified data processing on large clusters. In: Proceedings of the 6th symposium on operating systems design and implementation, San Francisco, pp 137–150