jeangeezhang / Semi-Supervised-Lifelong-Learning-for-Sentiment-Classification

Lifelong machine learning is a novel machine learning paradigm which continually learns tasks and accumulates knowledge for reusing. The knowledge extracting and reusing abilities enable the lifelong machine learning to understand the knowledge for solving a task and obtain the ability to solve the related problems. In sentiment classification, traditional approaches like Naïve Bayes focus on the probability for each words with positive or negative sentiment. However, the lifelong machine learning in this paper will investigate this problem in a different angle and attempt to discover which words determine the sentiment of a review. We will pay all attention to obtain knowledge during learning for future learning rather than just solve a current task.

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Semi-Unsupervised-Lifelong-Learning-for-Sentiment

Lifelong machine learning is a novel machine learning paradigm which continually learns tasks and accumulates knowledge for reusing. The knowledge extracting and reusing abilities enable the lifelong machine learning to understand the knowledge for solving a task and obtain the ability to solve the related problems. In sentiment classification, traditional approaches like Naïve Bayes focus on the probability for each words with positive or negative sentiment. However, the lifelong machine learning in this paper will investigate this problem in a different angle and attempt to discover which words determine the sentiment of a review. We will pay all attention to obtain knowledge during learning for future learning rather than just solve a current task.

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Lifelong machine learning is a novel machine learning paradigm which continually learns tasks and accumulates knowledge for reusing. The knowledge extracting and reusing abilities enable the lifelong machine learning to understand the knowledge for solving a task and obtain the ability to solve the related problems. In sentiment classification, traditional approaches like Naïve Bayes focus on the probability for each words with positive or negative sentiment. However, the lifelong machine learning in this paper will investigate this problem in a different angle and attempt to discover which words determine the sentiment of a review. We will pay all attention to obtain knowledge during learning for future learning rather than just solve a current task.


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