Sentiment analysis of tweets using refined neutrosophic sets

Abstract In the last decade, opinion mining and sentiment analysis have been the subject of fascinating interdisciplinary research. Alongside the evolution of social media networks, the sheer volume of social media text available for sentiment analysis has increased multi-fold, leading to a formidable corpus. Sentiment analysis of tweets have been carried out to gauge public opinion on breaking news, various policies, legislations, personalities and social movements. Fuzzy logic has been used in the sentiment analysis of twitter data, whereas neutrosophy which factors in the concept of indeterminacy has not been used to analyse tweets. In this paper, the concept of multi refined neutrosophic set (MRNS) with two positive, three indeterminate and two negative memberships is proposed. Single valued neutrosophic set (SVNS), triple refined indeterminate neutrosophic set (TRINS) and MRNS have been used in the sentiment analysis of tweets on ten different topics. Eight of these topics chosen for sentiment analysis are related to Indian scenario and two topics to international scenario. A comparative analysis of the methods show that the approach with MRNS provides better refinement to the indeterminacy present in the data.

[1]  Rajshekhar Sunderraman,et al.  Single Valued Neutrosophic Sets , 2010 .

[2]  Nilanjan Dey,et al.  A survey of the state-of-the-arts on neutrosophic sets in biomedical diagnoses , 2019, Int. J. Mach. Learn. Cybern..

[3]  Florentin Smarandache,et al.  FUZZY COGNITIVE MAPS AND NEUTROSOPHIC COGNITIVE MAPS , 2003, math/0311063.

[4]  Jun Ye,et al.  Similarity measures between interval neutrosophic sets and their applications in multicriteria decision-making , 2014, J. Intell. Fuzzy Syst..

[5]  W. B. Vasantha Kandasamy,et al.  Fuzzy Neutrosophic Models for Social Scientists , 2020 .

[6]  Hong-yu Zhang,et al.  A projection-based TODIM method under multi-valued neutrosophic environments and its application in personnel selection , 2016, Neural Computing and Applications.

[7]  Krassimir T. Atanassov,et al.  Intuitionistic fuzzy sets , 1986 .

[8]  Yanhui Guo,et al.  Color texture image segmentation based on neutrosophic set and wavelet transformation , 2011, Comput. Vis. Image Underst..

[9]  Paolo Rosso,et al.  On the difficulty of automatically detecting irony: beyond a simple case of negation , 2014, Knowledge and Information Systems.

[10]  M. G. Bhatt,et al.  Development of Fuzzy-Single Valued Neutrosophic MADM Technique to Improve Performance in Manufacturing and Supply Chain Functions , 2018, Fuzzy Multi-criteria Decision-Making Using Neutrosophic Sets.

[11]  Erik Cambria,et al.  Recent Trends in Deep Learning Based Natural Language Processing , 2017, IEEE Comput. Intell. Mag..

[12]  Peiyu Liu,et al.  Capsule Network With Identifying Transferable Knowledge for Cross-Domain Sentiment Classification , 2019, IEEE Access.

[13]  Le Hoang Son,et al.  A Neutrosophic Recommender System for Medical Diagnosis Based on Algebraic Neutrosophic Measures , 2016, Appl. Soft Comput..

[14]  Ilanthenral Kandasamy,et al.  Double-Valued Neutrosophic Sets, their Minimum Spanning Trees, and Clustering Algorithm , 2018, J. Intell. Syst..

[15]  Paolo Rosso,et al.  Irony detection via sentiment-based transfer learning , 2019, Inf. Process. Manag..

[16]  Erkki Sutinen,et al.  Are They Different? Affect, Feeling, Emotion, Sentiment, and Opinion Detection in Text , 2014, IEEE Transactions on Affective Computing.

[17]  Mika V. Mäntylä,et al.  The evolution of sentiment analysis - A review of research topics, venues, and top cited papers , 2016, Comput. Sci. Rev..

[18]  Paolo Rosso,et al.  Irony, Sarcasm, and Sentiment Analysis , 2017 .

[19]  Craig J. Russell,et al.  Moderated Regression Analysis and Likert Scales: Too Coarse for Comfort , 1992 .

[20]  F. Smarandache,et al.  Multicriteria Decision Making Using Double Refined Indeterminacy Neutrosophic Cross Entropy and Indeterminacy Based Cross Entropy , 2016 .

[21]  Florentin Smarandache,et al.  A Group Decision Making Framework Based on Neutrosophic TOPSIS Approach for Smart Medical Device Selection , 2019, Journal of Medical Systems.

[22]  Peide Liu,et al.  Multiple attribute decision-making method based on single-valued neutrosophic normalized weighted Bonferroni mean , 2014, Neural Computing and Applications.

[23]  Peide Liu,et al.  Multiple attribute group decision making methods based on some normal neutrosophic number Heronian Mean operators , 2017, J. Intell. Fuzzy Syst..

[24]  Erik Cambria,et al.  Targeted Aspect-Based Sentiment Analysis via Embedding Commonsense Knowledge into an Attentive LSTM , 2018, AAAI.

[25]  Uzay Kaymak,et al.  Multi-lingual support for lexicon-based sentiment analysis guided by semantics , 2014, Decis. Support Syst..

[26]  Paolo Rosso,et al.  Mining Subjective Knowledge from Customer Reviews: A Specific Case of Irony Detection , 2011, WASSA@ACL.

[27]  Nguyen Thanh Tung,et al.  Segmentation of dental X-ray images in medical imaging using neutrosophic orthogonal matrices , 2018, Expert Syst. Appl..

[28]  Preslav Nakov,et al.  SemEval-2013 Task 2: Sentiment Analysis in Twitter , 2013, *SEMEVAL.

[29]  Ilanthenral Kandasamy,et al.  Triple Refined Indeterminate Neutrosophic Sets for personality classification , 2016, 2016 IEEE Symposium Series on Computational Intelligence (SSCI).

[30]  Ilanthenral Kandasamy,et al.  Indeterminate Likert scale: feedback based on neutrosophy, its distance measures and clustering algorithm , 2019, Soft Comput..

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

[32]  Peide Liu,et al.  Multiple attribute decision-making method based on some normal neutrosophic Bonferroni mean operators , 2015, Neural Computing and Applications.

[33]  Jun Ye,et al.  Multicriteria decision-making method using the correlation coefficient under single-valued neutrosophic environment , 2013, Int. J. Gen. Syst..

[34]  H. Speier.  Historical Development of Public Opinion , 1950, American Journal of Sociology.

[35]  David M. Pennock,et al.  Mining the peanut gallery: opinion extraction and semantic classification of product reviews , 2003, WWW '03.

[36]  Ying Wah Teh,et al.  Text mining for market prediction: A systematic review , 2014, Expert Syst. Appl..

[37]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

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

[39]  Md. Ansarul Haque,et al.  Sentiment Analysis by Using Fuzzy Logic , 2014, ArXiv.

[40]  Shuai Wang,et al.  Deep learning for sentiment analysis: A survey , 2018, WIREs Data Mining Knowl. Discov..

[41]  Peide Liu,et al.  The generalized hybrid weighted average operator based on interval neutrosophic hesitant set and its application to multiple attribute decision making , 2015, Neural Computing and Applications.

[42]  Heng-Da Cheng,et al.  A NEW NEUTROSOPHIC APPROACH TO IMAGE THRESHOLDING , 2008 .

[43]  Paolo Rosso,et al.  SemEval-2015 Task 11: Sentiment Analysis of Figurative Language in Twitter , 2015, *SEMEVAL.

[44]  Vincent A. Knight,et al.  Tweeting the terror: modelling the social media reaction to the Woolwich terrorist attack , 2014, Social Network Analysis and Mining.

[45]  Florentin Smarandache,et al.  A unifying field in logics : neutrosophic logic : neutrosophy, neutrosophic set, neutrosophic probability , 2020 .

[46]  Jun Ye,et al.  A multicriteria decision-making method using aggregation operators for simplified neutrosophic sets , 2014, J. Intell. Fuzzy Syst..

[47]  Florentin Smarandache,et al.  Bipolar Complex Neutrosophic Sets and Its Application in Decision Making Problem , 2018, Fuzzy Multi-criteria Decision-Making Using Neutrosophic Sets.

[48]  Victor I. Chang,et al.  Evaluation of the green supply chain management practices: A novel neutrosophic approach , 2019, Comput. Ind..

[49]  Jun Ye,et al.  Single valued neutrosophic cross-entropy for multicriteria decision making problems , 2014 .

[50]  Ming Zhang,et al.  A neutrosophic approach to image segmentation based on watershed method , 2010, Signal Process..