Twitter sentiment analysis using Machine Learning and Hadoop: A comparative study

Bhasin, Anshu; Das, Suman K.

doi:10.1109/icsccc51823.2021.9478077

Cited by 9 publications

(2 citation statements)

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“…Twitter is a widely used social network service, which users can post and interact with messages. Many sentiment analysis studies [4][5][6][7] use tweets as training data for machine learning. Some studies [8][9][10] focus on using sentiment analysis associated with tweets to assess the impact of coronavirus.…”

Section: Related Researchmentioning

confidence: 99%

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Sentiment-based Chatbot using Machine Learning for Recommendation System

Hsu

Liao

2022

Preprint

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Emotions are part of human mental activities and play an extremely important role in the decision-making process of daily life. Publishing posts in natural language through social websites is part of people’s lives. These posts can reflect the emotion state of users. It is important to study how to use machine learning technology to interpret the sentiment analysis of posts. The research proposed a general framework based on sentiment analysis and machine learning, called Sentiment Analysis and Machine Learning Recommendation Framework (SAMLRF), including data preparation module, sentiment analysis module, recommendation module, human machine module and cloud computing module for a chatbot to facilitate user interaction to make recommendations. To verify the modular function of the proposed SAMLRF, this research develops a Sentiment-based Article Recommendation Linebot (SARL), which provides an API interface for chatbots to activate the system through a webhook mechanism. The performance and accuracy of four machine learning and two deep learning algorithms were compared, including the decision tree, logistic regression, support vector machine and gradient boosting decision tree, simple recurrent neural networks and long short-term memory, operating in Spark cloud computing environments. Experiments show that the decision tree algorithm for sentiment analysis is relatively better in computing performance and test accuracy.

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Section: Related Researchmentioning

confidence: 99%

“…Linebot: What problems did the project encounter?(4). User: complain the project is difficult to do, and have to work overtime every day (5). Linebot: please talk something about work overtime (6).…”

Section: System Demonstrationmentioning

confidence: 99%