A Modified Metaheuristic Algorithm for Opinion Mining

Saraswathi, K.; Tamilarasi, A.

doi:10.5120/9329-3634

Cited by 5 publications

(2 citation statements)

References 17 publications

(11 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Their results revealed that the Genetic Algorithm exhibits better performance than Naive Bayes, and that a combined classifier outperforms single classifiers. In addition, Saraswathi and Tamilarasi [19] proposed a modified metaheuristic algorithm for opinion mining, which uses Bagging to predict opinions as positive or negative. Their results revealed that Bagging performed better than Naive Bayes and the CART algorithms.…”

Section: Related Workmentioning

confidence: 99%

A hybrid computational intelligence approach for efficiently evaluating customer sentiments in E-commerce reviews

Cosma

2014

2014 IEEE Symposium on Intelligent Agents (IA)

View full text Add to dashboard Cite

The Internet has opened new interesting scenarios in the fields of e-commerce, marketing and on-line transactions. In particular, thanks to mobile technologies, customers can make purchases in a faster and cheaper way than visiting stores, and business companies can increase their sales volume due to a world-wide visibility. Moreover, online trading systems allow customers to gather all the required information about product quality and characteristics, via customer reviews, and make an informed purchase. Due to the fact that these reviews are used to determine the extent of customers acceptance and satisfaction of a product or service, they can affect the future selling performance and market share of a company because they can also be used by companies to determine the success of a product, and predict its demand. As a consequence, tools for efficiently classifying textual customer reviews are becoming a key component of each e-commerce development framework to enable business companies to define the most suitable selling strategies and improve their market capabilities. This paper introduces an innovative framework for efficiently analysing customer sentiments in textual reviews in order to compute their corresponding numerical rating to allow companies to better plan their future business activities. The proposed approach addresses different issues involved in this significant task: the dimension and imprecision of ratings data. As shown in experimental results, the proposed hybrid approach yields better learning performance than other state of the art rating predictors. © 2014 IEEE

show abstract

Section: Related Workmentioning

confidence: 99%

A hybrid computational intelligence approach for efficiently evaluating customer sentiments in E-commerce reviews

Cosma

2014

2014 IEEE Symposium on Intelligent Agents (IA)

View full text Add to dashboard Cite

show abstract

“…The weighted sum is then passed through an activation function, which determines the output of the neuron. The activation function introduces non-linearity into the model and allows neural networks to learn complex patterns and relationships (Saraswathi and Tamilarasi, 2012;Yang, 2022).…”

Section: Indicators For Estimation Research Parametersmentioning

confidence: 99%

Real-Time Forecasting of Key Coking Coal Quality Parameters Using Neural Networks and Artificial Intelligence

Dyczko

2023

MGPB

View full text Add to dashboard Cite

High quality coke is a key raw material for the metallurgical industry. The characteristics of the coal have a significant influence on the parameters of the coke produced and, consequently, on the valuation of coal deposits and the economic assessment of mining projects. Predicting the quality of coking coal allows for the optimisation of production processes, including the planning and management of operations and the early detection of quality problems. In this study, using the principles of a smart mine, it is proposed to determine the quality of coal based on the combination of mining and geological conditions of mineral deposits and its quality indicators. Possible interrelationships between the quality of the coal in the deposit and the characteristics of the final product have been identified. A neural network is used to determine the priority of individual indicators that have a significant impact on the quality of coking coal. An important part of the research is its practical implementation in the conditions of the Jastrzębska Spółka Węglowa SA. Qualitative and quantitative parameters of coking coals were obtained for each mine of the region by the method of sampling and statistical processing of data such as: degree of metamorphism, thickness, deviation of volatile substances, presence of phosphorus, ash content, etc. For their evaluation, the Group Method of Data Handling was used to compare the factors of quality indicators depending on the priority of influence on the final characteristics of the coking coal. Based on the results obtained, it is shown that not all coal quality indicators have a significant impact on the quality of the final product. The study shows that it is possible to predict the main indicators (CRI – Coke Reactivity Index, CSR – Coke Strength after Reaction) of coke quality using neural networks based on a larger number of coal quality parameters and to eliminate parameters that have virtually no influence on the value of the final product. This method can also be used to improve the results of economic valuation of a deposit and to better plan exploration and mining operations.

show abstract

Metaheuristic and Evolutionary Methods for Feature Selection in Sentiment Analysis (a Comparative Study)

Ighazran

Chrifi‐Alaoui

Boujiha

2018

2018 International Symposium on Advanced Electrical and Communication Technologies (ISAECT)

View full text Add to dashboard Cite

A Modified Metaheuristic Algorithm for Opinion Mining

Cited by 5 publications

References 17 publications

A hybrid computational intelligence approach for efficiently evaluating customer sentiments in E-commerce reviews

A hybrid computational intelligence approach for efficiently evaluating customer sentiments in E-commerce reviews

Real-Time Forecasting of Key Coking Coal Quality Parameters Using Neural Networks and Artificial Intelligence

Metaheuristic and Evolutionary Methods for Feature Selection in Sentiment Analysis (a Comparative Study)

Contact Info

Product

Resources

About