Lexicon-based Sentiment Analysis Using the Particle Swarm Optimization

Machová, Kristína; Mikula, Martin; Gao, Xiaoying; Mach, Marián

doi:10.3390/electronics9081317

Cited by 31 publications

(15 citation statements)

References 22 publications

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“…Effectivity of lexicon approach, of course depends on the correct choice of words in the lexicon and their most accurate assessing with the degree of polarity. In our work [ 29 ] the effective lexicon-based application for sentiment analysis is described. For this application two lexicons were generated-Big and Small.…”

Section: Methodsmentioning

confidence: 99%

Comparison of Machine Learning and Sentiment Analysis in Detection of Suspicious Online Reviewers on Different Type of Data

Machová

Mach

Vasilko

2021

Sensors

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The article focuses on solving an important problem of detecting suspicious reviewers in online discussions on social networks. We have concentrated on a special type of suspicious authors, on trolls. We have used methods of machine learning for generation of detection models to discriminate a troll reviewer from a common reviewer, but also methods of sentiment analysis to recognize the sentiment typical for troll’s comments. The sentiment analysis can be provided also using machine learning or lexicon-based approach. We have used lexicon-based sentiment analysis for its better ability to detect a dictionary typical for troll authors. We have achieved Accuracy = 0.95 and F1 = 0.80 using sentiment analysis. The best results using machine learning methods were achieved by support vector machine, Accuracy = 0.986 and F1 = 0.988, using a dataset with the set of all selected attributes. We can conclude that detection model based on machine learning is more successful than lexicon-based sentiment analysis, but the difference in accuracy is not so large as in F1 measure.

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Section: Methodsmentioning

confidence: 99%

Comparison of Machine Learning and Sentiment Analysis in Detection of Suspicious Online Reviewers on Different Type of Data

Machová

Mach

Vasilko

2021

Sensors

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“…The sentiment scores of common words can be obtained from a lexicon (Musto et al 2015). The problem with the lexicon-based approach is that sentiment of sentences or texts which do not contain words from the lexicon cannot be successfully classified (Machová et al 2020). The degree of applicability of the lexicon to the text therefore determines the effectiveness of the lexicon-based approach.…”

Section: Methodsmentioning

confidence: 99%

Can Economic Growth Reduce Public Dissatisfaction? Evidence from a Panel Threshold Model in Chinese Cities

Liu

et al. 2022

Urban Affairs Review

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Much is known about the relationship between economic growth and satisfaction, while little is known about the relationship between economic growth and dissatisfaction. This study measures the levels of public dissatisfaction in 36 Chinese cities from 2011 to 2018 using data from an official online petition platform. We construct panel data to analyze the impact of per capita GDP and per capita income on public dissatisfaction. The results show that per capita income has a significant negative effect on public dissatisfaction, but only once the per capita income exceeds a threshold amount. In the extended discussion, the nonlinear relationship between income and public dissatisfaction is described as a stair-shaped curve. The results imply the need for developing countries to adhere to economic policies based on income maximization.

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“…Their study showed sentiment classification system with 92% accuracy. Machová et al (2020) used lexicon and Naïve Bayes to solve SA problem. They employed particle swarm optimization (PSO) for automatic labelling of words to develop their lexicon.…”

Section: Hybrid Approachmentioning

confidence: 99%

Statistical learning approaches to sentiment analysis in the Nigerian banking context

Ogunleye¹

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Banking is an essential component of our day-to-day activity, and the sector contributes to the development of every Nation's economy. To ensure bank stability, the banks have gone through several deregulation and reformation processes. Unfortunately, these processes and technological advancement have led to increased competitiveness, saturated market, and low profitability. Thus, the need for banks to build customer centric service to gain profitability and stability is vital. This opens the need to investigate customers attitude towards banking.With the growing usage of social network sites (SNS), the user generated content (UGC) has opened opportunities for banks to mine customers opinion . This can help the banks to generate insight into their product and services, create marketing strategies and manage their reputation. To the customers, this serves as source to information that can help in supporting decision making on product or service purchase. In this study, sentiment analysis (SA) techniques were employed to investigate customers attitude towards banking us ing Twitter data. However, the unstructured nature of the data and word ambiguity made sentiment analysis complicated. In the context of this study, SA is more difficult because it involves the natural language processing of Pidgin English and English words in the bank domain.Unfortunately, there are limited or no resources for this purpose.

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Lexicon-based Sentiment Analysis Using the Particle Swarm Optimization

Cited by 31 publications

References 22 publications

Comparison of Machine Learning and Sentiment Analysis in Detection of Suspicious Online Reviewers on Different Type of Data

Comparison of Machine Learning and Sentiment Analysis in Detection of Suspicious Online Reviewers on Different Type of Data

Can Economic Growth Reduce Public Dissatisfaction? Evidence from a Panel Threshold Model in Chinese Cities

Statistical learning approaches to sentiment analysis in the Nigerian banking context

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