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ailearning

AiLearning: 机器学习 - MachineLearning - ML、深度学习 - DeepLearning - DL、自然语言处理 NLP

aspect-based-sentiment-analysis

A paper list for aspect based sentiment analysis.

aspect-based-sentiment-analysis-1

💭 Aspect-Based-Sentiment-Analysis: Transformer & Explainable ML (TensorFlow)

contextualized-topic-models

A python package to run contextualized topic modeling. CTMs combine BERT with topic models to get coherent topics. Also supports multilingual tasks. Cross-lingual Zero-shot model published at EACL 2021.

employer-reviews-using-topic-modeling

glassdoor-company-review-analysis

Springboard Data Science course capstone project 2

glassdoor-topic-modeling-my-employer

insight-ds-project

NLP, topic modeling, and sentiment analysis of credit card customer reviews

lda_topic_modeling

Using LDA topic modeling and text features to predict "helpful votes" in Glassdoor reviews.

multilingual-text-classification--xlm-roberta

To build multilingual models with English-only training data to find the toxicity among Mutilingual Comments

pygrams

Extracts key terminology (n-grams) from any large collection of documents (>1000) and forecasts emergence

pytorch-sentiment-analysis

Tutorials on getting started with PyTorch and TorchText for sentiment analysis.

quantfin

A project for research on asset pricing

sentiment_analysis

project for 224U

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

text-mining-employer-brand-and-employee-satisfaction

The project aims to extract employer characteristics which are helping and hurting employee sentiment towards the employer and aims at providing a single source of truth related to employee satisfaction based on thousands of reviews on Glassdoor

weshclass

[AAAI 2019] Weakly-Supervised Hierarchical Text Classification

word2vec-sentiments

Tutorial for Sentiment Analysis using Doc2Vec in gensim (or "getting 87% accuracy in sentiment analysis in under 100 lines of code")