Sentiment Analysis on Cryptocurrency using Youtube Comments

Divesh, None; Prasad, G. Durga; Sharma, Gaurav; Vishwakarma, Dinesh Kumar

doi:10.1109/iccmc53470.2022.9753723

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…The methodology employed by Padmalatha et al [28] is based on Naive Bayes model to analyze social media opinions. Prasad et al [29] designed an ensemble classifier to classify YouTube comments based on cryptocurrency. They used Decision Tree, K Nearest Neighbors, Random Forest Classifier, XGBoost, and a Logistic Regression base classifier to create a stacked ensemble model.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Zero-Shot Crypto Sentiment With Fine-Tuned Language Model and Prompt Engineering

Wahidur,

Tashdeed,

Kaur

et al. 2024

IEEE Access

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Blockchain technology has revolutionized the financial landscape, witnessing widespread adoption of cryptocurrencies due to their decentralized and transparent nature. As sentiments expressed on social media platforms wield substantial influence over cryptocurrency market dynamics, sentiment analysis has emerged as a crucial tool for gauging public opinion and predicting market trends. This paper explores fine-tuning techniques for large language models to enhance sentiment analysis performance. Experimental results demonstrate a significant average zero-shot performance gain of 40% on unseen tasks after finetuning, highlighting its potential. Additionally, the impact of instruction-based fine-tuning on models of varying scales is examined, revealing that larger models benefit from instruction tuning, achieving the highest average accuracy score of 75.16%. In contrast, smaller-scale models may experience reduced generalization due to complete model capacity utilization. To gain deeper insight into instruction effectiveness, the paper presents experimental investigations under different instruction tuning setups. Results show the model achieves an average accuracy score of 72.38% for short and simple instructions, outperforming long and complex instructions by over 12%. Finally, the paper explores the relationship between fine-tuning corpus size and model performance, identifying an optimal corpus size of 6,000 data points for the highest performance across different models. Microsoft's MiniLM, a distilled version of BERT, excels in efficient data use and performance optimization, while Google's FLAN-T5 demonstrates consistent and reliable performance across diverse datasets.

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Section: Related Workmentioning

confidence: 99%

Enhancing Zero-Shot Crypto Sentiment With Fine-Tuned Language Model and Prompt Engineering

Wahidur,

Tashdeed,

Kaur

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Furthermore, the field of machine learning has seen significant advancements, with timeseries analysis being one of many areas experiencing substantial progress. For example, Prasad et al (2022) used the Youtube comment section as social media information source and fed these data into a stacked ensemble model consisting of a Decision Tree, K Nearest Neighbours, a Random Forest Classifier and XGBoost and a meta/base classifier-Logistic Regression, which achieved a 94.2% accuracy.…”

Section: Related Workmentioning

confidence: 99%

Pump It: Twitter Sentiment Analysis for Cryptocurrency Price Prediction

Koltun,

Yamshchikov

2023

Risks

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This study demonstrates the significant impact of market sentiment, derived from social media, on the daily price prediction of cryptocurrencies in both bull and bear markets. Through the analysis of approximately 567 thousand tweets related to twelve specific cryptocurrencies, we incorporate the sentiment extracted from these tweets along with daily price data into our prediction models. We test various algorithms, including ordinary least squares regression, long short-term memory network and neural hierarchical interpolation for time series forecasting (NHITS). All models show better performance once the sentiment is incorporated into the training data. Beyond merely assessing prediction error, we scrutinise the model performances in a practical setting by applying them to a basic trading algorithm managing three distinct portfolios: established tokens, emerging tokens, and meme tokens. While NHITS emerged as the top-performing model in terms of prediction error, its ability to generate returns is not as compelling.

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“…YouTube is a key participant among these platforms, garnering billions of people worldwide who interact with a vast spectrum of video material [1][2][3]. As YouTube's popularity grows, it has become a hotspot for user-generated material, including comments that represent the thoughts, views, and feedback of its massive user base [4][5][6][7]. Understanding and forecasting the mood of YouTube comments is becoming more important for a variety of stakeholders, including content producers, platform administrators, and advertisers [8][9].…”

Section: Introductionmentioning

confidence: 99%

Youtube Comment Feature Selection And Classification Using Fused Machine Learning

R. Rooba,

2023

tjjpt

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The exponential rise of internet platforms, notably YouTube, has resulted in a massive volume of user-generated material, including video comments. Understanding audience input and enhancing user experience require analyzing and forecasting the mood of YouTube comments. This work provides a comprehensive method for YouTube comment prediction and sentiment classification that combines feature selection using Recursive Feature Elimination (RFE), Elastic Net Random Forest with Logistic Regression (RF with LR), and Principal Component Analysis (PCA). The first stage is to choose the most informative features from a given dataset using RFE, a common approach for doing so. RFE aids in the elimination of unnecessary or redundant features, resulting in enhanced model performance and decreased computing complexity. The Elastic Net Random Forest with Logistic Regression (RF with LR) technique is then used to construct a robust sentiment classification model. Elastic Net regularization combines the advantages of both L1 (Lasso) and L2 (Ridge) regularization, allowing for improved feature selection and management of multicollinearity concerns. By integrating many decision trees, the Random Forest ensemble approach improves the model's predictive potential even more. We employ Principal Component Analysis (PCA) to increase the classification model's effectiveness and solve possible difficulties created by high-dimensional data. PCA decreases the dataset's complexity while retaining its fundamental qualities, resulting in a more manageable and efficient feature space for classification. Finally, we compare the performance of three prominent classifiers on the preprocessed dataset: Linear Support Vector Machine (LSVM), Gaussian Naive Bayes (GNB), Logistic Regression (LR), and Decision Tree (DT). We can select the best-performing model for YouTube comment categorization by comparing these classifiers.

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Sentiment Analysis on Cryptocurrency using Youtube Comments

Cited by 5 publications

References 11 publications

Enhancing Zero-Shot Crypto Sentiment With Fine-Tuned Language Model and Prompt Engineering

Enhancing Zero-Shot Crypto Sentiment With Fine-Tuned Language Model and Prompt Engineering

Pump It: Twitter Sentiment Analysis for Cryptocurrency Price Prediction

Youtube Comment Feature Selection And Classification Using Fused Machine Learning

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