Social media trend analysis to predict the success of products using deep learning technique

Social media trend analysis to predict the success of products using deep learning technique

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.

Bibliografiske detaljer
Main Authors:	Khan, Farden Ehsan, Ruhan, Ahmed Mahir, Shamsuddin, Rifat
Andre forfattere:	Ashraf, Faisal Bin
Format:	Thesis
Sprog:	English
Udgivet:	Brac University 2023
Fag:	Social media Trend analysis Deep learning Sentiment analysis KNN Text mining MLP RoBERTa Random forest BART Decision Tree DistilBERT Social media > Economic aspects Machine learning
Online adgang:	http://hdl.handle.net/10361/21814

Lignende værker

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

Quality assessment of extracted information from newspaper comment sections using natural language processing
af: Deb, Arnob, et al.
Udgivet: (2024)

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

References validation in scholarly articles using RoBERTa
af: Nasib, Abdullah Umar
Udgivet: (2023)

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

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

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

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

Predicting suicidal intent from social media text post using machine learning
af: Chowdhury, Md. Mubin Ul Islam, et al.
Udgivet: (2023)

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

A counseling system to predict the study path for freshmen
af: Usha, Rowshni Tasneem, et al.
Udgivet: (2019)

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

A feature selection approach to determine obesity using machine learning method
af: Antor, Shahidul Alam, et al.
Udgivet: (2021)

Predicting COVID-19 disease outcome and post-recovery conditions using machine learning
af: Sajid, Abul Kasem, et al.
Udgivet: (2021)

Cyberbullying Detection using Machine Learning from Social Media comments in Bangla Language
af: Tuhin, Saikat Halder, et al.
Udgivet: (2023)

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)

A hybrid rumor detection model derived from a comparative study of supervised approaches
af: Aothoi, Mehzabin Sadat, et al.
Udgivet: (2023)

Machine learning based analysis and prediction of crop yield and prices of Aman, Aus and Boro rice
af: Bhattacharjee, Ruposri, et al.
Udgivet: (2024)

A machine learning approach to analyze and predict rainfall in different regions of Bangladesh
af: Rahee, Arnob, et al.
Udgivet: (2021)

Analyzing the security of e-Health data based on a hybrid federated learning model
af: Shafin, Md. Mehtabul Islam, et al.
Udgivet: (2023)

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

Predicting brain age from EEG signals using machine learning and neural network
af: Pratanu, Abul Mushfique Muslah, et al.
Udgivet: (2023)

Analyzing Schizophrenic-prone text from social media content: a novel approach through ML and NLP
af: Rodela, Raisa Rahman, et al.
Udgivet: (2024)

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

Text classification using machine learning algorithms
af: Hasnat, Fahim, et al.
Udgivet: (2018)

Social media terraforming media landscape
af: Zayed, Abdullah
Udgivet: (2021)

SCAMM: detection and prevention of SQL injection attacks using a machine learning approach
af: Alam, Auninda, et al.
Udgivet: (2021)

Hate speech detection using machine learning techniques
af: Manzoor, Tahbib, et al.
Udgivet: (2024)

An analytic framework on food trend of Dhaka city using social media reviews and real time data set
af: Alif, Redwanul Alam
Udgivet: (2017)

Predicting regional accents of Bengali language using deep learning
af: Ibrahim, Md. Abu, et al.
Udgivet: (2022)

The applications of data mining and machine learning in Bangladesh for disease pattern analysis and prediction
af: Mahmud, Mahmudul Hasan
Udgivet: (2021)

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

A machine learning-based approach for data analysis to ascertain suicidal individuals from Social media users
af: Nahar, Fatiha Binte Kamrun, et al.
Udgivet: (2023)

Cryptocurrency price prediction using Social Media Data Mining and Epidemic Modeling
af: Safa, Bushra, et al.
Udgivet: (2023)

Classification and Explanation of Different Internet of Things (IoT) Network Attacks using Machine Learning, Deep Learning and XAI
af: Tasnim, Anika, et al.
Udgivet: (2022)

Parking data analysis
af: Ahamed, Kasfi, et al.
Udgivet: (2023)

A color vision approach considering Reflection Co efficient based on Autoencoder techniques using deep neural networks
af: Mahmud, Shakib Izaz, et al.
Udgivet: (2022)

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

Modelling and forecasting energy demand of Bangladesh using AI based algorithms
af: Mahmud, Booshra Nazifa, et al.
Udgivet: (2019)

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