06P_Sentiment-Analysis-With-Deep-Learning-Using-BERT and Text-Sentiment-Analysis
These are competitors—both provide standalone implementations of neural sentiment classification (BERT fine-tuning vs. RNN/Transformer architectures), targeting the same use case without dependency on each other.
About 06P_Sentiment-Analysis-With-Deep-Learning-Using-BERT
mohd-faizy/06P_Sentiment-Analysis-With-Deep-Learning-Using-BERT
Finetuning BERT in PyTorch for sentiment analysis.
This project helps you understand customer opinions from text data, like social media comments or product reviews, by classifying them as positive or negative. It takes raw text as input and outputs the sentiment, helping marketing analysts, product managers, or customer service teams quickly gauge public perception. The core of this tool is a fine-tuned BERT model, designed for accurate natural language processing.
About Text-Sentiment-Analysis
LuluW8071/Text-Sentiment-Analysis
Text Sentiment Analysis with RNNs Models + Additive Attention and Transformers
This tool helps businesses and analysts understand opinions expressed in text by automatically categorizing written feedback as positive, negative, or neutral. You feed it customer reviews, social media posts, or survey responses, and it tells you the underlying sentiment. It's ideal for anyone who needs to quickly gauge public opinion or customer satisfaction from large volumes of text.
Scores updated daily from GitHub, PyPI, and npm data. How scores work