1 line for thousands of State of The Art NLP models in hundreds of languages The fastest and most accurate way to solve text problems.
-
Updated
May 21, 2024 - Python
1 line for thousands of State of The Art NLP models in hundreds of languages The fastest and most accurate way to solve text problems.
Repository with all what is necessary for sentiment analysis and related areas
This project focuses on sentiment analysis. Social Sentiment analysis is the use of natural language processing (NLP) to analyze social conversations online and determine deeper context as they apply to a topic, brand or theme.
An emotion-polarity classifier specifically trained on developers' communication channels
练手项目:Comment of Interest 电商文本评论数据挖掘 (爬虫 + 观点抽取 + 句子级和观点级情感分析)
Sentiment analysis for Twitter and social media
Automated NLP sentiment predictions- batteries included, or use your own data
SentiSE is a sentiment analysis tool for Software Engineering interactions
Sentiment Analysis Symposium 2015
Build a Movie Reviews Sentiment Classifier with Google's BERT Language Model
Sentiment Analysis of Yelp Review Dataset using Hugging Face pre-trained BERT with fine-tuning
Creating a sentiment analysis machine learning model in python
A system that analyzes the tone of texts and statements.
Twitter Sentiment Analysis
Sentiment Analysis with HuggingFace Transformers Pipeline with 5 Lines of Python code
Twitter data on US Airlines Sentiment Analysis with Deep Learning (LSTM and CNN)
Sentiment Analysis on Whatsapp Chat
Sentiment Analysis using BERT embeddings + MLP (Multi Layer Perceptron). Served as A REST API in FastAPI and running in Docker.
Multi-classification Sentiment analysis on Web-Scraped Amazon product reviews using NLP (with nltk, TextBlob, VADER) along with various ML and DL (RoBERTa) models.
Add a description, image, and links to the sentiment-classifier topic page so that developers can more easily learn about it.
To associate your repository with the sentiment-classifier topic, visit your repo's landing page and select "manage topics."