Sentiment analysis of tweets using refined neutrosophic sets

Kandasamy, Ilanthenral; Vasantha, W. B.; Obbineni, Jagan Mohan; Smarandache, Florentín

doi:10.1016/j.compind.2019.103180

Cited by 37 publications

(23 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then, under the guidance of multigranulation probabilistic models, we utilize the model of INSs, MGRSs and PRSs to handle the above-mentioned challenges. Moreover, compared with existing popular nonlinear modeling approaches, such as formal concept analysis [33,34,58,[64][65][66][67], control systems [59,60] and sentiment analysis [61][62][63]68,69], which neither effectively handle indeterminate and incomplete information in complicated MAGDM problems, nor reasonably fuse and analyze multi-source information with incorrect and noisy data, it is necessary to combine INSs, MGRSs with PRSs to develop some meaningful hybrid models along with corresponding MAGDM approaches. In light of MAGDM procedures in the current section, we sum up the merits of the proposed MAGDM algorithm below:…”

Section: Discussionmentioning

confidence: 99%

“…Finally, a real-world example is employed to prove the validity of the established decision-making rule. In addition, it is noteworthy that plenty of interesting nonlinear modeling approaches have been proved to be successful in various applications [58][59][60][61][62][63][64][65][66][67]. For instance, Medina and Ojeda-Aciego [58] applied multi-adjoint frameworks to general t-concept lattice, and some other works on fuzzy formal contexts based on GrC-based approaches were explored in succession [64][65][66][67].…”

Section: The Contributions Of the Researchmentioning

confidence: 99%

“…Gil et al [60] studied a surrogate model based optimization of traffic lights cycles and green period ratios by means of microscopic simulation and fuzzy rule interpolation. Smarandache et al [61] explored word-level sentiment similarities in the context of NSs, and some other meaningful works on word-level sentiment analysis were also investigated recently [62,63,68,69].…”

Section: The Contributions Of the Researchmentioning

confidence: 99%

See 2 more Smart Citations

Multi-Attribute Group Decision Making Based on Multigranulation Probabilistic Models with Interval-Valued Neutrosophic Information

Zhang

Kang

et al. 2020

Mathematics

View full text Add to dashboard Cite

In plenty of realistic situations, multi-attribute group decision-making (MAGDM) is ubiquitous and significant in daily activities of individuals and organizations. Among diverse tools for coping with MAGDM, granular computing-based approaches constitute a series of viable and efficient theories by means of multi-view problem solving strategies. In this paper, in order to handle MAGDM issues with interval-valued neutrosophic (IN) information, we adopt one of the granular computing (GrC)-based approaches, known as multigranulation probabilistic models, to address IN MAGDM problems. More specifically, after revisiting the related fundamental knowledge, three types of IN multigranulation probabilistic models are designed at first. Then, some key properties of the developed theoretical models are explored. Afterwards, a MAGDM algorithm for merger and acquisition target selections (M&A TSs) with IN information is summed up. Finally, a real-life case study together with several detailed discussions is investigated to present the validity of the developed models.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: The Contributions Of the Researchmentioning

confidence: 99%

Section: The Contributions Of the Researchmentioning

confidence: 99%

See 1 more Smart Citation

Multi-Attribute Group Decision Making Based on Multigranulation Probabilistic Models with Interval-Valued Neutrosophic Information

Zhang

Kang

et al. 2020

Mathematics

View full text Add to dashboard Cite

show abstract

“…These SVNS are then visualized using clustering algorithms such as K-means and K-NN. Neutrosophic refined sets [10,[14][15][16] have been developed and applied in various fields, including in sentiment analysis recently. However no one has till now attempted to do speech sentiment analysis using neutrosophy and combine it with text sentiment analysis.…”

Section: Literature Surveymentioning

confidence: 99%

“…The various distance measures and clustering algorithms defined over neutrosophic sets are given in [2,11,14].…”

Section: Neutrosophymentioning

confidence: 99%

A Novel Framework Using Neutrosophy for Integrated Speech and Text Sentiment Analysis

Mishra

Kandasamy

et al. 2020

Symmetry

Self Cite

View full text Add to dashboard Cite

With increasing data on the Internet, it is becoming difficult to analyze every bit and make sure it can be used efficiently for all the businesses. One useful technique using Natural Language Processing (NLP) is sentiment analysis. Various algorithms can be used to classify textual data based on various scales ranging from just positive-negative, positive-neutral-negative to a wide spectrum of emotions. While a lot of work has been done on text, only a lesser amount of research has been done on audio datasets. An audio file contains more features that can be extracted from its amplitude and frequency than a plain text file. The neutrosophic set is symmetric in nature, and similarly refined neutrosophic set that has the refined indeterminacies I1 and I2 in the middle between the extremes Truth T and False F. Neutrosophy which deals with the concept of indeterminacy is another not so explored topic in NLP. Though neutrosophy has been used in sentiment analysis of textual data, it has not been used in speech sentiment analysis. We have proposed a novel framework that performs sentiment analysis on audio files by calculating their Single-Valued Neutrosophic Sets (SVNS) and clustering them into positive-neutral-negative and combines these results with those obtained by performing sentiment analysis on the text files of those audio.

show abstract

Single‐valued neutrosophic similarity measure‐based additive ratio assessment framework for optimal site selection of electric vehicle charging station

Mishra¹,

Rani

Saha

2021

Int J Intell Syst

View full text Add to dashboard Cite

Sustainable site selection for electric vehicle charging station (EVCS) is a significant process in the promotion of electric vehicle system development. The assessment and selection of suitable EVCS site is a very critical decision, involving complexity due to the presence of several associated criteria. Furthermore, uncertainty is an inevitable component of the information in the decision-making procedure and its significance in the selection process is relatively high and needs to be cautiously measured. Single-valued neutrosophic set (SVNS) is one of the valuable and flexible tools for handling such type of uncertain information arising in multi-criteria decision-making (MCDM) applications. Thus, the objective of this study is to introduce novel single-valued neutrosophic information-based additive ratio assessment (ARAS) approach for evaluating and prioritizing the sustainable EVCS sites. In this method, novel single-valued subjective and objective weighted integrated approach (SVN-SOWIA) is developed to compute the criteria by aggregating the objective weights resulted from a similarity measure-based procedure and the subjective weights given by the experts. For this purpose, an innovative similarity measure is proposed for SVNSs.To display the performance of the present methodology, a computational study of EVCS sites evaluation is conferred under single-valued neutrosophic environment. Comparative and sensitivity analyses are further performed to verify the strength of the developed approach. The outcome illustrates EVCS site EvUrjaa-Electric Vehicle Charging Station is the most optimal EVCS site in Indore region, India. Also, the environmental (0.324) and social (0.273) criteria are more important than technological (0.236) and economical (0.167) criteria in assessing the EVCS sites. The sensitivity analysis outcomes signify the EVCS option EvUrjaa-Electric Vehicle Charging Station always acquires its highest ranking in spite of how sub-criteria weights fluctuate. The outcome of this study indicates that the developed approach can suggest more realistic performance under uncertain environment and therefore, provides a wide range of applications. K E Y W O R D Sadditive ratio assessment, electric vehicle charging station, multi-criteria decision-making, similarity measure, single-valued neutrosophic set | INTRODUCTIONDue to the rapid urbanization and depletion of natural resources, environmental degradation is a vital issue in today's world. 1 Rapid growth in population, urban development, modernization of agriculture, rapid industrialization, multiplicity of the means of transport, and deforestation are main causes of the environmental degradation. Energy is currently a crucial constraint on transport and transport is a foremost determinant of energy demand. To reduce air pollution and protect the environment, several countries have implemented different strategies to promote electric vehicle (EV) production. 2 EV is regarded as an innovative eco-friendly means of transportation which encourages the secu...

show abstract

Sentiment analysis of tweets using refined neutrosophic sets

Cited by 37 publications

References 41 publications

Multi-Attribute Group Decision Making Based on Multigranulation Probabilistic Models with Interval-Valued Neutrosophic Information

Multi-Attribute Group Decision Making Based on Multigranulation Probabilistic Models with Interval-Valued Neutrosophic Information

A Novel Framework Using Neutrosophy for Integrated Speech and Text Sentiment Analysis

Single‐valued neutrosophic similarity measure‐based additive ratio assessment framework for optimal site selection of electric vehicle charging station

Contact Info

Product

Resources

About