The project aims to conduct a comparative analysis of baseline and deep learning models on sentiment analysis using a twitter dataset. We also studied the impact of inclusion of emoticons in our analysis, which is the novelty of our project.
📚🧠🌐 Welcome to TextAIHub repository! Explore the fascinating realm of NLP, text generation, sentiment analysis, and beyond. Join us in propelling language understanding to new frontiers through state-of-the-art AI models and advanced techniques. Together, let's ignite a revolution in text processing! 🚀💬🌍
Code to extract Twitter KPIs & Sentiment Analysis for Tweets
An end-to-end NLP project of mine.
Ask Ubuntu Logs analysis with Hadoop, MapReduce 2(Yarn)
Crypto stock trader.
A sentiment analysis in Hebrew based on HeBert (https://github.com/avichaychriqui/HeBERT) of WhatsApp groups!
Just another bunch of code to test DL for SentimentAnalysis (mainly Spanish)
Twitter Sentiment Analysis - work completed during the semester at The University of Melbourne in Machine Learning (COMP30027)
Exploring topic modeling techniques, sentiment analysis, and classification algorithms using the Kaggle dataset "Tripadvisor Reviews 2023"
This project provides a simple script for determining the sentiment of a text input using TextBlob library in Python. It also returns the most positive and most negative sentence in the input text. The script can be used as a standalone tool or integrated into other projects.
Sentiment analysis on tweets, news articles and other sources to quantify the Psychological Impact of COVID-19 and lock-down on people for India
Toolset for checking differences in recognising semantic relation presence by: (1) large language models 🤖 and (2) annotators / experts ✍️
This is a simple sentiment analysis project using Java. With explanation of methods and objects.
Funções e dicionários em forma de pacote para o R, criados a partir da necessidade de realização de de uma Análise de Sentimento em português.
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.
Conducted sentiment analysis on Amazon reviews using natural language processing techniques and Transformers model to classify reviews as positive, negative, or neutral.
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