zdmc23/sentiment-analysis-arabic
A deep learning (LSTM) sentiment analysis project to determine positive/negative sentiment in Arabic social media content.
This project helps you understand public opinion by analyzing Arabic social media posts. It takes Arabic text, such as tweets, and determines whether the sentiment expressed is positive or negative. This is ideal for social media managers, market researchers, or anyone needing to gauge sentiment from Arabic online discussions.
No commits in the last 6 months.
Use this if you need to automatically categorize Arabic social media content as having a positive or negative sentiment.
Not ideal if you require nuanced sentiment (e.g., neutral, mixed emotions) or sentiment analysis for languages other than Arabic.
Stars
25
Forks
22
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Sep 22, 2018
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/embeddings/zdmc23/sentiment-analysis-arabic"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
codelion/adaptive-classifier
A flexible, adaptive classification system for dynamic text classification
jiegzhan/multi-class-text-classification-cnn-rnn
Classify Kaggle San Francisco Crime Description into 39 classes. Build the model with CNN, RNN...
jiegzhan/multi-class-text-classification-cnn
Classify Kaggle Consumer Finance Complaints into 11 classes. Build the model with CNN...
cbaziotis/datastories-semeval2017-task4
Deep-learning model presented in "DataStories at SemEval-2017 Task 4: Deep LSTM with Attention...
iamaziz/ar-embeddings
Sentiment Analysis for Arabic Text (tweets, reviews, and standard Arabic) using word2vec