Sentiment analysis using R: an approach to correlate bitcoin price fluctuations with change in user sentiments

Sentiment analysis using R: an approach to correlate bitcoin price fluctuations with change in user sentiments

This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018.

Bibliografiske detaljer
Main Authors:	Rahman, Shaomi, Hemel, Jonayed Nafis, Anta, Syed Junayed Ahmed, Al Muhee, Hossain
Andre forfattere:	Uddin, Jia
Format:	Thesis
Sprog:	English
Udgivet:	BRAC University 2018
Fag:	Bitcoin Price fluctuations User sentiments Sentiment analysis R programmimg language
Online adgang:	http://hdl.handle.net/10361/10163

Lignende værker

Analyzing users’ sentiment towards video games based on reviews from microblog
af: Roy, Abhijeet, et al.
Udgivet: (2018)

Unveiling underlying patterns, drivers and anomalies in cryptocurrency price dynamics through feature fusion of financial indicators and sentiment fluctuations
af: Rabbi, Md. Nafis
Udgivet: (2024)

A deep learning approach to predict crypto-currency price by evaluating sentiment and stock market correlations
af: Maliha, Miftahul Zannat, et al.
Udgivet: (2023)

Microblog sentiment analysis for rating TV shows
af: Abdullah, Ashik
Udgivet: (2017)

Sentiment analysis on mobile operators’ customer review
af: Hossain, Mohammed Mohibul, et al.
Udgivet: (2020)

Sentiment analysis for Bengali newspaper headlines
af: Hossain, Mohammad Samman, et al.
Udgivet: (2017)

A multimodal approach of sentiment analysis to predict customer emotions towards a product
af: Islam, Shadman, et al.
Udgivet: (2020)

A transformer based approach to detect the sentiment of drivers in ride sharing platforms
af: Chakraborty, Sovon
Udgivet: (2024)

Forecasting bitcoin price considering macro economic factors and media influence using bidirectional LSTM and random forest regressor as ensemble model
af: Ghose, Swattic, et al.
Udgivet: (2023)

Amazon product reviews sentiment analysis using supervised learning algorithms
af: Hawladar, Mohibullah, et al.
Udgivet: (2021)

Sentimental analysis on political speeches
af: Atiq, Asif, et al.
Udgivet: (2022)

Performing sentiment analysis in Bangla microblog posts
af: Chowdhury, Shaika, et al.
Udgivet: (2017)

Product rating generation based on public opinion using sentiment analysis
af: Rabbi, Reshan Ahmed, et al.
Udgivet: (2018)

Feature based mobile phone rating using sentiment analysis and machine learning approaches
af: Kafi, Abdullahil, et al.
Udgivet: (2018)

Optimizing restaurant recommendations through sentiment analysis
af: Juha, Tasnia, et al.
Udgivet: (2024)

Embedded system based bangla intelligent social virtual robot with sentiment analysis
af: Hossain, Md. Ishan Arefin, et al.
Udgivet: (2018)

Sentimental analysis of customer product Reviews to understand customer needs using machine learning
af: Symum, Md Abdullah Al, et al.
Udgivet: (2023)

An effective machine learning approach for sentiment analysis of restaurant reviews
af: Karim, Rabita
Udgivet: (2018)

Analyzing data of social media to evaluate customer behavior of companies using sentiment analysis
af: Shourin, Bushra, et al.
Udgivet: (2018)

Aspect-based sentiment analysis using SemEval and Amazon datasets
af: Hasib, Tamanna, et al.
Udgivet: (2018)

A sustainable Bitcoin architecture
af: Monem, Maruf
Udgivet: (2022)

A framework for sentiment analysis: a data-driven approach
af: Islam, Md. Jahedul, et al.
Udgivet: (2021)

Human sentiment analysis using natural language processing
af: Shamya, Md. Shamiul Haque Khan, et al.
Udgivet: (2024)

Sentiment classification on Bengali food and restaurant reviews
af: Hossain, Abid, et al.
Udgivet: (2024)

Sentiment analysis in Bengali Text using NLP
af: Sarkar, Ankon, et al.
Udgivet: (2023)

Predicting stock market movement using sentiment analysis of twitter feed
af: Chakraborty, Pranjal, et al.
Udgivet: (2017)

Bang-lish sentiment classification using deep learning
af: Saleheen, Abrar, et al.
Udgivet: (2023)

Share market forecasting with LSTM neural network and sentimental trend prediction
af: Rony, Ismail Hossain, et al.
Udgivet: (2020)

Stock Market Price movement prediction using RNN and Point-weight Sentiment
af: Uddin, S. M. Rageeb Noor, et al.
Udgivet: (2022)

Sentiment analysis on Bangladesh airlines review data using machine learning
af: Hasib, Khan Md.
Udgivet: (2022)

Social media sentiment study on COVID-19 outbreak
af: Karmaker, Anik, et al.
Udgivet: (2021)

Sentiment analysis of customer reviews on food ordering portals of Bangladesh using natural language processing
af: Deb, Priom, et al.
Udgivet: (2024)

Into the heart of Bangla speech: advancing speech sentiment recognition with semi-supervised multimodal machine learning model leveraging an iterative SHAP-based feature selection
af: Shruti, Abanti Chakraborty
Udgivet: (2024)

Bitcoin price forecasting based on historical data
af: Roy, Shaily, et al.
Udgivet: (2018)

Fluctuating nonlinear oscillators

Inflation Dynamics in Advanced Economies : A Decomposition into Cyclical and Non-Cyclical Factors /
af: Lian, Weicheng
Udgivet: (2022)

Sentiments
af: Matimba

Drift in Producer Price Indexes for the Former Soviet Union (FSU) Countries /
af: Zieschang, Kimberly
Udgivet: (1994)

A machine learning approach to predict movie box-office success
af: Quader, Nahid, et al.
Udgivet: (2018)

Sentimental Opera
af: Stefano Castelvecchi
Udgivet: (2013)