sedflix/unsacmt
Unsupervised Sentiment Analysis for Code-mixed Data
This project helps social media analysts and brand managers understand public opinion expressed in conversations that mix multiple languages, like Hindi and English. It takes social media posts, comments, or customer feedback that contain code-mixed text as input. The output is a sentiment score (positive, negative, or neutral) for each piece of text, helping identify trends or specific issues. Anyone monitoring public sentiment or customer feedback in multilingual online environments would find this useful.
No commits in the last 6 months.
Use this if you need to analyze the emotional tone of text written in a blend of two or more languages, without requiring pre-labeled training data.
Not ideal if your data is purely monolingual or if you require fine-grained emotion detection beyond general sentiment.
Stars
8
Forks
4
Language
Jupyter Notebook
License
—
Category
Last pushed
Feb 04, 2020
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/embeddings/sedflix/unsacmt"
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