Twitter Sentiment Analysis Using Supervised Machine Learning

Yadav, Nikhil; Kudale, Omkar; Rao, Aditi; Gupta, Srishti; Shitole, Ajitkumar

doi:10.1007/978-981-15-9509-7_51

Cited by 34 publications

(11 citation statements)

References 8 publications

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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%

“…User: complain the project is difficult to do, and have to work overtime every day (5). Linebot: please talk something about work overtime (6). User: hope to successfully complete the project work on schedule (7).…”

Section: System Demonstrationmentioning

confidence: 99%

Sentiment-based Chatbot using Machine Learning for Recommendation System

Hsu

Liao

2022

Preprint

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“…In [22], authors have used sentiment and emotion classification to determine the avalanche point of an epidemic outbreak. The common thing in many of the recent studies regarding sentiment analysis is the use of Twitter as the primary source of data [23] [24] [25]. It is owing to the fact that Twitter is a universal microblogging website and it allows the users to express their thoughts in limited characters which makes the preprocessing part easy for the researchers [26].…”

Section: Related Workmentioning

confidence: 99%

Improving Hate Speech Detection of Urdu Tweets Using Sentiment Analysis

et al. 2021

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Sentiment Analysis is a technique that is being used abundantly nowadays for customer reviews analysis, popularity analysis of electoral candidates, hate speech detection and similar applications. Sentiment analysis on tweets encounters challenges such as highly skewed classes, high dimensional feature vectors and highly sparse data. In this study, we have analyzed the improvement achieved by successively addressing these problems in order to determine their severity for sentiment analysis of tweets. Firstly, we prepared a comprehensive data set consisting of Urdu Tweets for sentiment analysis-based hate speech detection. To improve the performance of the sentiment classifier, we employed dynamic stop words filtering, Variable Global Feature Selection Scheme (VGFSS) and Synthetic Minority Optimization Technique (SMOTE) to handle the sparsity, dimensionality and class imbalance problems respectively. We used two machine learning algorithms i.e., Support Vector Machines (SVM) and Multinomial Naïve Bayes' (MNB) for investigating performance in our experiments. Our results show that addressing class skew along with alleviating the high dimensionality problem brings about the maximum improvement in the overall performance of the sentiment analysis-based hate speech detection.

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“…Some of these features are removed such as stop words, while the other features are replaced such as URL and User mention. Lemmatization is also applied where the words are transformed to their base form [10]. Feature vector representation allows the classifier to perform the classification process efficiently [11].…”

Section: Text Preprocessing and Feature Extraction Techniquesmentioning

confidence: 99%

Twitter Sentiment Analysis Using Different Machine Learning and Feature Extraction Techniques

Habib

Sultani

2021

ANJS

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Twitter is considered a significant source of exchanging information and opinion in today's business. Analysis of this data is critical and complex due to the size of the dataset. Sentiment Analysis is adopted to understand and analyze the sentiment of such data. In this paper, a Machine learning approach is employed for analyzing the data into positive or negative sentiment (opinion). Different arrangements of preprocessing techniques are applied to clean the tweets, and various feature extraction methods are used to extract and reduce the dimension of the tweets' feature vector. Sentiment140 dataset is used, and it consists of sentiment labels and tweets, so supervised machine learning models are used, specifically Logistic Regression, Naive Bayes, and Support Vector Machine. According to the experimental results, Logistic Regression was the best amongst other models with all feature extraction techniques.

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Twitter Sentiment Analysis Using Supervised Machine Learning

Cited by 34 publications

References 8 publications

Sentiment-based Chatbot using Machine Learning for Recommendation System

Sentiment-based Chatbot using Machine Learning for Recommendation System

Improving Hate Speech Detection of Urdu Tweets Using Sentiment Analysis

Twitter Sentiment Analysis Using Different Machine Learning and Feature Extraction Techniques

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