This project performed sentimental analysis based on opinion words (like good, bad, beautiful, wrong, best, awesome, etc) of selected opinion target ( like product name for amazon product reviews).
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Updated
Jul 26, 2018 - Python
This project performed sentimental analysis based on opinion words (like good, bad, beautiful, wrong, best, awesome, etc) of selected opinion target ( like product name for amazon product reviews).
Quy Nhon AI Hackathon 2022 - Challenge 2: Review Analytics - Top 1 Solution
The Amaon Fine Foods Review dataset consists of reviews of fine foods from Amazon. There are approximate 500,000 reviews up to October 2012. Reviews include product and user information, ratings, and a plain text review. The Aim of this case study was to predict the polarity of the reviews ie. positive/negative. I have applied various Machine Le…
Game Review Analysis in Steam for 2019 HYU Social Network Analysis and Text Mining Term Project
LADy 💃: A Benchmark Toolkit for Latent Aspect Detection Enriched with Backtranslation Augmentation
This project aims to analyze consumer sentiment towards (FMCG) company products by scraping reviews & performing text analysis using Python. By leveraging NLP techniques, such as sentiment analysis, word cloud and topic modelling. The results of this study can inform product development, marketing strategies & overall business decision-making
Extract customer reviews from some online stores and classify negative reviews.
Opinion classification with kili-technology and huggingface by fine-tuning roBERTa model.
2021 Introduction-to-Information-Retrieval-and-Text-Mining Final Project
A Machine Learning model for sentiment analysis of Amazon reviews that can predict the customer sentiments based on their product reviews
An AI solution which cognitively able to detect(classify) reviews in fractions of seconds. hence, fewer human interventions, more precise, uniform results, and most importantly operational efficiency.
Analysing Amazon customer reviews via Clustering, Visualization and Classification
This repository contains the code for a rating review classification project that was submitted for the Kaggle Wars competition hosted by ACM Thapar. The project aims to classify reviews based on their rating, using data pre-processing and a convolutional neural network (CNN) model.
This project uses Machine Learning, Natural Language Processing (NLP), and Web Scraping in order to get real customer reviews for any product on Amazon and perform sentiment analysis that predicts whether the reviews are positive or negative.
Ecommerce analysis
Review Analysis (NLP) of musical instrument reviews from the amazon dataset. Observing performances of Linear SVC, NaiveBayes (MultinomialNB) , Random Forest Classifier and Logistic Regression under use of Count and tf-idf vectorizers.
Get your scrapes in sync
This has been done as a part of Data Science Career Track Course at Springboard.
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