A gradient boosted decision tree-based sentiment classification of twitter data

In India, the banks have a formidable edge in maintaining their customer retention ratio for past few decades. Downfall makes the private banks to reduce their operations and the nationalised banks merge with other banks. The researchers have used the traditional and ensemble algorithms with relevant feature engineering techniques to better classify the customers. The proposed algorithm uses a Meta classifier instead of an ensemble algorithm with an adaptive genetic algorithm for feature selection. Churn prediction is the number of customers who wants to terminate their services in the banking sector. The model considers twelve attributes like credit score, geography, gender, age, etc, to predict customer churn. The project consists of five modules as follows. First is the pre-processing module that identifies the missing data and fills the value with mean and mode. Second is the data transformation module where, the categorical data is converted into numerical data using label encoding to fasten the computations. The converted numerical data is normalized using the standard scalar technique. The feature selection module identifies the essential attributes using DragonFly and Firefly (Hybrid Fly) algorithms. The classification module designs an intelligent Meta learner, which combines the Ensemble Algorithm Extreme Gradient Boosting (XGBOOST) with base classifiers as "Extra Tree Classifier" and "Logistic Regression" to predict the churn customers.

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“…Decision Trees [4] Decision Tree is the traditional algorithm that suffers from an overfitting problem. 4…”

Section: S Neelakandanmentioning

confidence: 99%

Adaptive XGBOOST Hyper Tuned Meta Classifier for Prediction of Churn Customers

Srikanth¹,

Papineni²,

Sridevi³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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“…Yang et al analyzed the sentiment of environmental public service microblog, and the XGBoost model had the highest prediction accuracy [17]. Neelakandan et al conducted sentiment analysis on the text of social network platform Twitter and found that the GBDT algorithm has the best classification effect [18].…”

Section: Related Researchmentioning

confidence: 99%

Sentiment Classification of Crowdsourcing Participants’ Reviews Text Based on LDA Topic Model

et al. 2021

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The review text received by crowdsourcing participants contains valuable knowledge, opinions, and preferences, which is an important basis for employers to make trading decisions, and crowdsourcing participants to improve service level and quality. However, there are two kinds of emotional polarity in the review text, the attention paid to sentiment classification of review text with fuzzy emotional boundaries is insufficient. This paper proposes a supervised text sentiment classification method with Latent Dirichlet Allocation (LDA) to improve the classification performance of review texts with fuzzy sentiment boundaries. Taking the text reviews of crowdsourcing participants on the Zhubajie platform as the data set, using N-gram, Word2vec, and TF-IDF algorithms to extract text features. The LDA topic model is applied to expand the number of text features and extract eight topics that affect employers' sentiment tendencies. Text classifiers are constructed based on Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Decision Tree (GDBT), and Extreme Gradient Boosting (XGBoost) algorithms, and the effectiveness of the sentiment classification methods are verified by ten-fold crossvalidation and confusion matrix. Experimental results show that using the LDA topic model to extend the features of review text can effectively alleviate the problem that the classifier is difficult to distinguish the sentiment categories of different emotion polarity words coexisting text, and enhance the ability of emotion boundary fuzzy text classification. The GBDT text sentiment classifier based on TF-IDF and LDA has the best performance in accuracy, recall, and F1-measure.INDEX TERMS sentiment analysis; text classification; LDA topic model; crowdsourcing participants

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“…Neelakandan and Paulraj [14] start their sentiment analysis method by preprocessing the tweet. This preprocessing includes removing stop words and hashtags.…”

Section: Related Workmentioning

confidence: 99%

Building a Twitter Sentiment Analysis System with Recurrent Neural Networks

Nistor

Moca²,

Moldovan

et al. 2021

Sensors

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This paper presents a sentiment analysis solution on tweets using Recurrent Neural Networks (RNNs). The method is can classifying tweets with an 80.74% accuracy rate, considering a binary task, after experimenting with 20 different design approaches. The solution integrates an attention mechanism aiming to enhance the network, with a two-way localization system: at memory cell level and at network level. We present an in-depth literature review for Twitter sentiment analysis and the building blocks that grounded the design decisions of our solution, employed as a core classification component within a sentiment indicator of the SynergyCrowds platform.

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A gradient boosted decision tree-based sentiment classification of twitter data

Cited by 71 publications

References 21 publications

Adaptive XGBOOST Hyper Tuned Meta Classifier for Prediction of Churn Customers

Adaptive XGBOOST Hyper Tuned Meta Classifier for Prediction of Churn Customers

Sentiment Classification of Crowdsourcing Participants’ Reviews Text Based on LDA Topic Model

Building a Twitter Sentiment Analysis System with Recurrent Neural Networks

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