Sentiment Analysis in Twitter Social Network Centered on Cryptocurrencies Using Machine Learning

• URL: http://arxiv.org/abs/2501.09777v1
• Date: Thu, 16 Jan 2025 16:15:52 GMT
• Title: Sentiment Analysis in Twitter Social Network Centered on Cryptocurrencies Using Machine Learning
• Authors: Vahid Amiri, Mahmood Ahmadi,
• Abstract summary: This paper investigates the opinions of Iranian users on the Twitter social network about cryptocurrencies. It provides the best model for classifying tweets based on sentiment.
• Score: 1.9336815376402718
• License:
• Abstract: Cryptocurrency is a digital currency that uses blockchain technology with secure encryption. Due to the decentralization of these currencies, traditional monetary systems and the capital market of each they, can influence a society. Therefore, due to the importance of the issue, the need to understand public opinion and analyze people's opinions in this regard increases. To understand the opinions and views of people about different topics, you can take help from social networks because they are a rich source of opinions. The Twitter social network is one of the main platforms where users discuss various topics, therefore, in the shortest time and with the lowest cost, the opinion of the community can be measured on this social network. Twitter Sentiment Analysis (TSA) is a field that analyzes the sentiment expressed in tweets. Considering that most of TSA's research efforts on cryptocurrencies are focused on English language, the purpose of this paper is to investigate the opinions of Iranian users on the Twitter social network about cryptocurrencies and provide the best model for classifying tweets based on sentiment. In the case of automatic analysis of tweets, managers and officials in the field of economy can gain knowledge from the general public's point of view about this issue and use the information obtained in order to properly manage this phenomenon. For this purpose, in this paper, in order to build emotion classification models, natural language processing techniques such as bag of words (BOW) and FastText for text vectorization and classical machine learning algorithms including KNN, SVM and Adaboost learning methods Deep including LSTM and BERT model were used for classification, and finally BERT linguistic model had the best accuracy with 83.50%.

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