Predicting stock market trend from twitter feed and building a framework for Bangladesh

Predicting stock market trend from twitter feed and building a framework for Bangladesh

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

Bibliografiset tiedot
Päätekijät:	Karim, Shabab, Abdullah, Tahmid, Tayaba, Umme
Muut tekijät:	Majumdar, Mahbub Alam
Aineistotyyppi:	Opinnäyte
Kieli:	English
Julkaistu:	BRAC University 2018
Aiheet:	Stock market Sentiment analysis Classi er Regression Machine learning Logistic regression Tweets
Linkit:	http://hdl.handle.net/10361/10153

Samankaltaisia teoksia

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

Stock market prediction using ensemble learning
Tekijä: Rabbani, Rakin Bin, et al.
Julkaistu: (2021)

A voice signal based gender prediction model using random forest classifier
Tekijä: Ahmed, Saif, et al.
Julkaistu: (2018)

Forecasting stock market prices using advanced tools of machine learning
Tekijä: Anwar, Md. Tawhid, et al.
Julkaistu: (2019)

Sensitiveness of stock market returns to macroeconomic determinants; an empirical study on the pharmaceutical industry of Dhaka Stock Exchange
Tekijä: Tasrin, Ibnat
Julkaistu: (2024)

Flood prediction using ensemble machine learning models
Tekijä: Rahman, Tanvir
Julkaistu: (2024)

A thesis on utilizing machine learning models to predict material hardship
Tekijä: Das, Tirtha
Julkaistu: (2024)

Nonparametric bootstrapping for multiple logistic regression model using R
Tekijä: Hossain, Ahmed, et al.
Julkaistu: (2010)

Heart disease prediction system
Tekijä: Amit, Tayab Al Azad, et al.
Julkaistu: (2022)

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

Evaluating user influence in Twitter based on hashtags using data mining
Tekijä: Neha, Maheen Absar, et al.
Julkaistu: (2018)

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

Applied regression : an introduction /
Tekijä: Lewis-Beck, Michael S.
Julkaistu: (1980)

Logistic regression : a self-learning text /
Tekijä: Kleinbaum, David G.
Julkaistu: (2002)

Prediction of bipolar disorder from mental episodes using machine learning approach
Tekijä: Tasmeem, Sumaiya, et al.
Julkaistu: (2023)

Application of machine learning techniques on the context of predicting upcoming traffic congestion and providing the best preferred path
Tekijä: Saquib, Muhammad Sadman, et al.
Julkaistu: (2019)

Predicting the length of stay in a hospital for a particular disease using machine learning algorithms
Tekijä: Imam, Saif
Julkaistu: (2021)

Early grade prediction using profile data
Tekijä: Iqbal, Sumaiya, et al.
Julkaistu: (2021)

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

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

Detecting tweet sentiment and sarcasm on online learning during covid-19
Tekijä: Das, Shishir Kumar, et al.
Julkaistu: (2024)

Heart disease prediction using techniques of classification in machine learning
Tekijä: Afrose, Sadia, et al.
Julkaistu: (2021)

A supervised learning approach by machine learning and deep learning algorithms to predict type II DM risk
Tekijä: Farabe, Abdullah Al, et al.
Julkaistu: (2021)

Performance analysis of machine learning classi ers for detecting PE malware
Tekijä: Azmee, ABM.Adnan, et al.
Julkaistu: (2020)

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

Mathematical modeling of health service utilization data using multiple logistic regression
Tekijä: Shrestha, Gauri, et al.
Julkaistu: (2012)

A novel approach to reduce air pollution through machine learning based PM2.5 prediction
Tekijä: Alif, Omar Farhad, et al.
Julkaistu: (2023)

Stock price prediction
Tekijä: Das, Kaushik, et al.
Julkaistu: (2023)

Analysis on the effects of wallstreetbets on the stock market
Tekijä: Hossain, Abir, et al.
Julkaistu: (2022)

Statistical analysis of network data flows and predictions using statistical and machine learning regression models
Tekijä: Boateng, Albert, et al.
Julkaistu: (2024)

Performance analysis of machine learning algorithms in resume recommendation systems
Tekijä: Hasan, Ibteaz, et al.
Julkaistu: (2018)

An advanced hospital rating system using machine learning and natural language processing
Tekijä: Tamjid, Ali Asgar, et al.
Julkaistu: (2023)

Text classification with an efficient preprocessing technique for cross-language and multilingual data
Tekijä: Khan, Towhid, et al.
Julkaistu: (2023)

Polycystic ovary syndrome detection using neural network.
Tekijä: Istiyaq, Tahsin, et al.
Julkaistu: (2024)

Performance analysis of different machine learning approaches for single modal facial expression detection
Tekijä: Zinia, Surovi, et al.
Julkaistu: (2018)

Where will you setup your business next?: a machine learning approach to suggest ideal geographical location for new restaurant establishment
Tekijä: Shihab, Ibne Farabi, et al.
Julkaistu: (2018)

Prediction of diabetes induced complications using different machine learning algorithms
Tekijä: Rahman, Tahsinur, et al.
Julkaistu: (2018)

Flood prediction using machine learning models
Tekijä: Syeed, Miah Mohammad Asif, et al.
Julkaistu: (2022)

Public vehicle ETA system using machine learning
Tekijä: Kabir, Md. Miraz, et al.
Julkaistu: (2018)

Nonrecursive causal models /
Tekijä: Berry, William Dale
Julkaistu: (1984)