IhabBendidi/sentiment_embeddings
A scientific benchmark and comparison of the performance of sentiment analysis models in NLP on small to medium datasets
This project helps data scientists and NLP researchers compare how well different sentiment analysis models perform on smaller datasets. It takes raw text data, processes it, and then evaluates models like BERT, LSTM, and TextBlob to show their accuracy in classifying sentiment. The output provides insights into which models are most effective for specific sentiment analysis tasks.
No commits in the last 6 months.
Use this if you need to choose the best sentiment analysis model for your text data and want to see a clear comparison of various models' performance.
Not ideal if you're looking for a production-ready sentiment analysis API or a tool to analyze extremely large, streaming datasets.
Stars
13
Forks
4
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Dec 14, 2020
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/nlp/IhabBendidi/sentiment_embeddings"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
IBM/MAX-Text-Sentiment-Classifier
Detect the sentiment captured in short pieces of text
pabitralenka/Customer-Feedback-Analysis
Multi Class Text (Feedback) Classification using CNN, GRU Network and pre trained Word2Vec...
giuseppebonaccorso/twitter_sentiment_analysis_word2vec_convnet
Twitter Sentiment Analysis with Gensim Word2Vec and Keras Convolutional Network
danielegrattarola/twitter-sentiment-cnn
An implementation in TensorFlow of a convolutional neural network (CNN) to perform sentiment...
hpanwar08/sentiment-analysis-torchtext
Seniment Analysis in Torchtext