Enhanced sentiment analysis based on improved word embeddings and XGboost

Abstract

<span lang="EN-US">Sentiment analysis is a well-known and rapidly expanding study topic in natural language processing (NLP) and text classification. This approach has evolved into a critical component of many applications, including politics, business, advertising, and marketing. Most current research focuses on obtaining sentiment features through lexical and syntactic analysis. Word embeddings explicitly express these characteristics. This article proposes a novel method, improved words vector for sentiments analysis (IWVS), using XGboost to improve the F1-score of sentiment classification. The proposed method constructed sentiment vectors by averaging the word embeddings (Sentiment2Vec). We also investigated the Polarized lexicon for classifying positive and negative sentiments. The sentiment vectors formed a feature space to which the examined sentiment text was mapped to. Those features were input into the chosen classifier (XGboost). We compared the F1-score of sentiment classification using our method via different machine learning models and sentiment datasets. We compare the quality of our proposition to that of baseline models, term frequency-inverse document frequency (TF-IDF) and Doc2vec, and the results show that IWVS performs better on the F1-measure for sentiment classification. At the same time, XGBoost with IWVS features was the best model in our evaluation.</span>

Predictive maintenance framework for assessing health state of centrifugal pumps

“…Overall results confirmed the efficiency of the proposed framework following feature selection and XGBoost algorithm implementation in industrial applications. Furthermore, authors' findings aligned with previous research where XGBoost was effectively applied in socioeconomical aspects namely medicine [33], [34], economy [35], cybersecurity [36], language processing [37] and environmental applications [38]. Regarding medical applications, feature selection and XGBoost was considered the most effective solution for heart disease classification with 99.6% accuracy [39] improving the solution of [40] where the proposed decision trees provided 97.75% accuracy.…”

“…Furthermore, in [56] multilayer perceptron (MLP) slightly outperformed the XGBoost methodology with 99.3 % precision and was suggested by the authors as the optimal solution. Moreover, regarding language processing, XGBoost was proposed for sentiment features selection [37] and text similarity identification [57] with an F1 score of 69% and 89% respectively, whereas in [58] ANN was selected for human speech recognition with 77% precision. Finally, in terms of environmental applications, [59] combined a grid search algorithm and XGBoost model for hyperparameter fine tuning and electricity load prediction respectively, similarly [60] proved that ensemble techniques provide an efficient solution for solar radiation forecasting.…”

Sentiment analysis using naive bayes for reviews of visitors to Padang City beach tourism after the COVID-19 pandemic

“…Sentiment is an opinion or view that is based on exaggerated feelings towards something [17]. Sentiment analysis or opinion mining refers to a broad field of natural language processing, computational linguistics, and text mining that aims to analyze a person's opinions, sentiments, evaluations, attitudes, judgments,s and emotions whether speakers or writers regarding a topic, product, service, organization, individual, or specific activity [22].…”

“…Sentiment analysis is a computerized technology that can help and analyze a textual opinion sentence which works by understanding and extracting it like text mining to produce sentiment information [16][17][18]. Sentiment analysis, also called opinion mining, is a field of study that analyzes opinions, sentiments, evaluations, judgments, attitudes, and emotions towards entities such as products, services, organizations, individuals, issues, events, topics, and their attributes [19].…”