Amazon product reviews sentiment analysis using supervised learning algorithms

Amazon product reviews sentiment analysis using supervised learning algorithms

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.

Bibliografski detalji
Glavni autori:	Hawladar, Mohibullah, Ghosh, Arjan, Raad, Zaoyad Khan, Chowdhury, Wali Ahad, Shehan, Md Sazzad Hossain
Daljnji autori:	Ashraf, Faisal Bin
Format:	Disertacija
Jezik:	English
Izdano:	Brac University 2021
Teme:	Sentiment Analysis Opinion Mining Amazon Review Analysis Support Vector Machine (SVM) Decision tree Logistic Regression MLP Random Forest Analysis Sentiment analysis.
Online pristup:	http://hdl.handle.net/10361/14970

Similar Items

Aspect-based sentiment analysis using SemEval and Amazon datasets
od: Hasib, Tamanna, i dr.
Izdano: (2018)

Sentiment analysis for Bengali newspaper headlines
od: Hossain, Mohammad Samman, i dr.
Izdano: (2017)

Sentiment analysis on mobile operators’ customer review
od: Hossain, Mohammed Mohibul, i dr.
Izdano: (2020)

Predicting stock market movement using sentiment analysis of twitter feed
od: Chakraborty, Pranjal, i dr.
Izdano: (2017)

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

An advanced hospital rating system using machine learning and natural language processing
od: Tamjid, Ali Asgar, i dr.
Izdano: (2023)

A machine learning approach to predict movie box-office success
od: Quader, Nahid, i dr.
Izdano: (2018)

Analyzing users’ sentiment towards video games based on reviews from microblog
od: Roy, Abhijeet, i dr.
Izdano: (2018)

Analyzing data of social media to evaluate customer behavior of companies using sentiment analysis
od: Shourin, Bushra, i dr.
Izdano: (2018)

Sentiment analysis using R: an approach to correlate bitcoin price fluctuations with change in user sentiments
od: Rahman, Shaomi, i dr.
Izdano: (2018)

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

A multimodal approach of sentiment analysis to predict customer emotions towards a product
od: Islam, Shadman, i dr.
Izdano: (2020)

Performing sentiment analysis in Bangla microblog posts
od: Chowdhury, Shaika, i dr.
Izdano: (2017)

Analysis on the effects of wallstreetbets on the stock market
od: Hossain, Abir, i dr.
Izdano: (2022)

Optimizing restaurant recommendations through sentiment analysis
od: Juha, Tasnia, i dr.
Izdano: (2024)

Microblog sentiment analysis for rating TV shows
od: Abdullah, Ashik
Izdano: (2017)

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

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

Social media trend analysis to predict the success of products using deep learning technique
od: Khan, Farden Ehsan, i dr.
Izdano: (2023)

Predicting stock market trend from twitter feed and building a framework for Bangladesh
od: Karim, Shabab, i dr.
Izdano: (2018)

Text classification with an efficient preprocessing technique for cross-language and multilingual data
od: Khan, Towhid, i dr.
Izdano: (2023)

A voice signal based gender prediction model using random forest classifier
od: Ahmed, Saif, i dr.
Izdano: (2018)

Heart disease prediction system
od: Amit, Tayab Al Azad, i dr.
Izdano: (2022)

Feature based mobile phone rating using sentiment analysis and machine learning approaches
od: Kafi, Abdullahil, i dr.
Izdano: (2018)

Product rating generation based on public opinion using sentiment analysis
od: Rabbi, Reshan Ahmed, i dr.
Izdano: (2018)

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

Sentimental analysis of customer product Reviews to understand customer needs using machine learning
od: Symum, Md Abdullah Al, i dr.
Izdano: (2023)

Embedded system based bangla intelligent social virtual robot with sentiment analysis
od: Hossain, Md. Ishan Arefin, i dr.
Izdano: (2018)

Sentimental analysis on political speeches
od: Atiq, Asif, i dr.
Izdano: (2022)

A framework for sentiment analysis: a data-driven approach
od: Islam, Md. Jahedul, i dr.
Izdano: (2021)

Forecasting bitcoin price considering macro economic factors and media influence using bidirectional LSTM and random forest regressor as ensemble model
od: Ghose, Swattic, i dr.
Izdano: (2023)

A supervised learning approach by machine learning and deep learning algorithms to predict type II DM risk
od: Farabe, Abdullah Al, i dr.
Izdano: (2021)

A deep learning approach to predict crypto-currency price by evaluating sentiment and stock market correlations
od: Maliha, Miftahul Zannat, i dr.
Izdano: (2023)

Prediction of bipolar disorder from mental episodes using machine learning approach
od: Tasmeem, Sumaiya, i dr.
Izdano: (2023)

Sentiment analysis of customer reviews on food ordering portals of Bangladesh using natural language processing
od: Deb, Priom, i dr.
Izdano: (2024)

Share market forecasting with LSTM neural network and sentimental trend prediction
od: Rony, Ismail Hossain, i dr.
Izdano: (2020)

Sentiment classification on Bengali food and restaurant reviews
od: Hossain, Abid, i dr.
Izdano: (2024)

Supervising vehicle using pattern recognition
od: Lushan, Mihodi Hasan, i dr.
Izdano: (2018)

Sentiment analysis in Bengali Text using NLP
od: Sarkar, Ankon, i dr.
Izdano: (2023)

A machine learning approach to analyze and predict rainfall in different regions of Bangladesh
od: Rahee, Arnob, i dr.
Izdano: (2021)