Electronics · 2020 · 21 citations · 18 references
Customer SatisfactionEngineeringSentiment Analysis ModelCommunicationMultimodal Sentiment AnalysisSentiment AnalysisLanguage ProcessingText MiningNatural Language ProcessingCustomer ReviewInformation RetrievalComputational LinguisticsModel Analysis (Educational Assessment)Document ClassificationMood DefinitionLanguage StudiesContent AnalysisUkrainian TextUkrainian LanguageAutomatic ClassificationService ResearchKnowledge DiscoveryIntelligent ClassificationUser FeedbackMarketingModel Analysis (Information Engineering)Keyword ExtractionLinguistics
Sentiment analysis of Ukrainian text involves determining language and predicting sentiment scores. This study aims to develop a hybrid Ukrainian sentiment analyzer to improve mood classification accuracy and broaden language support in market tools. The authors constructed an ensemble of SVM, logistic regression, and XGBoost, augmented with a rule‑based algorithm, to classify Ukrainian Google Maps comments into food, hotels, museums, and shops, and to visualize the results. The hybrid model attains over 88 % accuracy and facilitates mining of positive and negative feedback, enabling electronics businesses to refine services based on frequent sentiment words.
The purpose of this paper is to develop a hybrid model Ukrainian language sentiment analyzer, which should improve the accuracy of the mood definition to expand the Ukrainian language among the instruments on the market. The object of research is the processes of determining the language of the text and predicting its sentiment score. The subject of the study is Ukrainian comments posted by Google Maps users. The following text categories are taken into account: food, hotels, museums, and shops. The new method was built as an ensemble of support vector machine, logistic regression, and XGBoost, in combination with a rule-based algorithm. The practical use of the algorithm makes it possible to analyze the Ukrainian text in accordance with the category with the visualization of the research results. The accuracy of the proposed method is bigger than 0.88 in the worst case. The mining procedure of the positive and negative sides of service providers based on users’ feedback is developed. It allows electronics business to make improvements based on frequent positive and negative words.
18
SenticNet 6: Ensemble Application of Symbolic and Subsymbolic AI for Sentiment Analysis
Erik Cambria, Yang Li, Frank Xing et al. · 2020 · 428 citations