Sentimentanalysis Python Demo
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Lexiconner | 74 | 4 years ago | 8 | apache-2.0 | Python | |||||
| Lexicon-based Named Entity Recognition | ||||||||||
| Php Nlgen | 48 | 6 months ago | 2 | September 06, 2020 | 3 | mit | PHP | |||
| NLGen: a library for creating recursive-descent natural language generators | ||||||||||
| Opinionmining | 12 | 4 years ago | 9 | Python | ||||||
| Opinion Mining/Sentiment Analysis Classifier using Genetic Programming | ||||||||||
| Sentimentanalysis Python Demo | 10 | 8 months ago | unlicense | Jupyter Notebook | ||||||
| Submission of an in-class NLP sentiment analysis competition held at Microsoft AI Singapore group. This submission entry explores the performance of both lexicon & machine-learning based models | ||||||||||
| Dialign | 8 | 7 months ago | 1 | other | Scala | |||||
| Automatic and generic measures of verbal alignment in dyadic dialogue based on sequential pattern mining at the level of surface of text utterances | ||||||||||
Exploration of Sentiment Analysis
This repo provides the submission entry for an in-class NLP sentiment analysis competition held at Microsoft AI Singapore group using techniques learned in class to classify text in identifying positive or negative sentiment.
Recommended to install Anaconda, a pre-packaged Python distribution that contains all of the necessary libraries and software for this project. Alternatively, you can make use of Google Colaboratory, which allows you to write and execute Python codes in your browser.
Data
Data for this in-class competition comes from the Sentiment140 dataset where the training and test data consists of randomly sampled 10% and 5% of the dataset.
Getting started using Lexicon and Machine Learning (ML) based methods
Open SentimentAnalysis.ipynb on a jupyter notebook environment, or 
- VADER (VALENCE based sentiment analyzer) [67%]
- Naive Bayes
- Linear SVM (Support Vector Machine) [80%]
- Decision Tree
- Random Forest
- Extra Trees
- SVC [80%]
Exploring using Deep Learning Techniques (LSTM)
Open SentimentAnalysis_RNN.ipynb on a jupyter notebook environment, or
The LSTM deep learning method [79%] did not perform better than SVC/SVM method
How about the BERT Transformers model?
Open SentimentAnalysis_BERT.ipynb on a jupyter notebook environment, or
The State-of-the-Art transformer model performs slightly better at [82%] accuracy
