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In the present day, the entertainment industry is constantly evolving toward making the most enjoyable and profitable sources of film entertainment. Through the use of movie rating sites, we can now decide whether or not it is worth the trip to the movie theatre to watch a partiuclar film. With this in mind, I wanted to explore what aspects of m…
Extract Video Game reviews from IGN's website using Beautiful Soup then apply Sentiment analysis using 3 pre-trained models including Hu and Lui, Vader and NRC
The system is implemented to scrape data from a booking website, perform Emotion Analysis on the reviews of the selected hotel and visualized the result over a time axis. R is used to implement the system and Shiny library is used to develop the Front-end.
This project aims to understand the sentiment when a bit policy is introduced by the government. I have used Twitter data to do sentiment analysis using R.
Empower your content moderation with the console based AI text filtering system. Seamlessly filter and flag inappropriate or harmful content with precision and efficiency.
This project uses machine learning to categorize and prioritize airline user tweets based on content and sentiment. The goal is to reduce airlines' workload and provide personalized, empathetic responses to users. By training a sentiment analysis model, airlines can better understand customers' needs and improve their overall service on Twitter.
Developed an Automated Twitter Response Tool for a focus in airline complaints using Kafka Streaming, LSTM, LDA, NRC Lexicon, and made analysis reports by using dataprep.ai