hmohebbi/SentimentAnalysis
(BOW, TF-IDF, Word2Vec, BERT) Word Embeddings + (SVM, Naive Bayes, Decision Tree, Random Forest) Base Classifiers + Pre-trained BERT on Tensorflow Hub + 1-D CNN and Bi-Directional LSTM on IMDB Movie Reviews Dataset
This project helps you understand the sentiment (positive or negative) of text, like customer reviews or social media comments. You provide raw text data, and it outputs a classification indicating the emotional tone. It's designed for data analysts, marketers, or researchers who need to quickly gauge public opinion or customer satisfaction from large volumes of text.
No commits in the last 6 months.
Use this if you need to analyze the sentiment of movie reviews or similar long-form text data and want to experiment with different, established machine learning approaches.
Not ideal if you're looking for a simple, out-of-the-box API for sentiment analysis on diverse or short-form text without needing to dive into the underlying models.
Stars
74
Forks
17
Language
Jupyter Notebook
License
—
Category
Last pushed
Nov 30, 2019
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/embeddings/hmohebbi/SentimentAnalysis"
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