sentiment140 (Kaggle Inc)
86
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Kaggle Inc
sentiment140
Sentiment140, supplied by Kaggle Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/sentiment140/sentiment140/pm41904902-426-18-46
Average 86 stars, based on 1 article reviews
Sentiment140, supplied by Kaggle Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/sentiment140/sentiment140/pm41904902-426-18-46
Average 86 stars, based on 1 article reviews
sentiment140 - by Bioz Stars,
2026-09
86/100 stars
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other:Article Title: A Stacking Ensemble Based on Lexicon and Machine Learning Methods for the Sentiment Analysis of Tweets Article Snippet: Data Availability Statement: The original data presented in the study are available in the Article Title: AI-Driven Sentiment Analysis for Enhanced Predictive Maintenance and Customer Insights in Enterprise Systems Article Snippet: Modern organizations and business environments are one step ahead of traditional ERP and CRM Enterprise systems to contribute to real-time analyses and interpretations of customers’ feedback for enhancing decision-making and maintenance mechanisms.. These gaps are filled in this paper by proposing the incorporation of AI-based sentiment analysis into Enterprise systems and employing state-of-the-art models, VADER and RoBERTa.. In this research, we offer a detailed process involving the integration of different components and data, a scrupulous data-gathering process, peculiar preprocessing measures, and optimal model deployment. Article Title: TFMPHGNN: Two-Fold multi-perspective heterogeneous graph neural network for sentiment analysis. Article Snippet: Sentiment analysis remains challenging due to the complex, intertwined relationships among sentiment expressions, contextual cues, and emotional features distributed across heterogeneous data sources.. Conventional deep learning and transformer-based models often treat sentiments as isolated units, failing to capture these rich, multi-perspective interactions.. To address these limitations, this study introduces a Two-Fold Multi-Perspective Heterogeneous Graph Neural Network (TFMPHGNN) that jointly models sentiment, emotion, and contextual dependencies within a dual-stage heterogeneous graph framework. Article Title: A Stacking Ensemble Based on Lexicon and Machine Learning Methods for the Sentiment Analysis of Tweets Article Snippet: Dataset The Article Title: A Stacking Ensemble Based on Lexicon and Machine Learning Methods for the Sentiment Analysis of Tweets Article Snippet: The Article Title: A Seed-Guided Latent Dirichlet Allocation Approach to Predict the Personality of Online Users Using the PEN Model Article Snippet: Data Availability Statement: Two publicly available dataset namely my Personality (the my Personality corpus provider had decided to stop providing the dataset since April 2018) and Article Title: Bitcoin volatility in bull vs . bear market-insights from analyzing on-chain metrics and Twitter posts Article Snippet: Data preprocessing. Selection:Article Title: How an Interest in Mindfulness Influences Linguistic Markers in Online Microblogging Discourse Article Snippet: .. It included a random selection of users from the Sentiment140, a publicly available dataset that was accessed through the online |