Predicting the usefulness of reviews given the review text and metadata surrounding the reviews.
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Updated
Oct 24, 2021 - Jupyter Notebook
Predicting the usefulness of reviews given the review text and metadata surrounding the reviews.
Scraping reviews of customer for specific product from e-commerce website like Amazon.com, Flipkart.com
this repo contains files for my analysis on disney land visitor reviews using NLP
Sentiment Analysis using LSTM model on the smartphone reviews. Which are are scarped from amazon.in .
Scraping of Trustpilot reviews, Analysis of the evolution of topics discussed in reviews over time.
Building better online reviews to empower users
ReviewsScoring is a system that can score and filter reviews from booking-hotels/booking-flats services based on ML and DL approaches.
This project will extract insights from user reviews of European airlines. For NLP analyis, I will models, e.g., TextBlob, VADER, RoBERTa, and Fair framework.
NLP
In this project, we will take analyze positive and negative hotel reviews and design a model for inferring positive or negative sentiment given a review. We combine a pretrained BERT lanuguage model and a custom multi-layer perceptron to achieve this.
This app searches reddit posts and comments to determine if a product or service has a positive or negative sentiment and predicts top product mentions using Named Entity Recognition
Sentiment analysis on the reviews of IMDB Dataset of top 1000 movies and tv shows.
Analyzed user sentiment for ChatGPT's new chat history feature. Found unexpected decrease in positive feedback, especially around data privacy. Highlights need for user-centered AI innovation.
Semantic analysis of winery customer reviews
Sentiment Analysis for Customer Reviews
GCP hosted product for over 1 million movie investors on HSX.com, aiding online movie trading and box-office investments by leveraging Big Data technologies like Hive and Hadoop, and Tableau dashboards
에어비앤비 텍스트 리뷰 감성 분석
Semantic analysis of reviews for element coffee using NLP
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