https://github.com/kwokhing/sentimentanalysis-python-demo

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
https://github.com/kwokhing/sentimentanalysis-python-demo

bert-model cross-validation data-analysis decision-trees deep-learning lexicon-based lstm-sentiment-analysis machine-learning machine-learning-algorithms naive-bayes naive-bayes-classifier random-forest sentiment-analysis sentiment-classification sklearn supervised-learning supervised-machine-learning support-vector-machines svm vader-sentiment-analysis

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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

• Host: GitHub
• URL: https://github.com/kwokhing/sentimentanalysis-python-demo
• Owner: KwokHing
• License: unlicense
• Created: 2019-09-13T03:13:31.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2022-10-10T12:53:07.000Z (over 2 years ago)
• Last Synced: 2023-08-19T08:42:09.610Z (over 1 year ago)
• Topics: bert-model, cross-validation, data-analysis, decision-trees, deep-learning, lexicon-based, lstm-sentiment-analysis, machine-learning, machine-learning-algorithms, naive-bayes, naive-bayes-classifier, random-forest, sentiment-analysis, sentiment-classification, sklearn, supervised-learning, supervised-machine-learning, support-vector-machines, svm, vader-sentiment-analysis
• Language: Jupyter Notebook
• Homepage:
• Size: 11.9 MB
• Stars: 12
• Watchers: 4
• Forks: 9
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
## 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.

![jpg](images/inclass-competition.jpg)

Recommended to install [Anaconda](https://www.anaconda.com/products/distribution), 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](https://colab.research.google.com/), which allows you to write and execute Python codes in your browser.

**Data**

Data for this in-class competition comes from the [Sentiment140](https://www.kaggle.com/datasets/kazanova/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 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://githubtocolab.com/KwokHing/SentimentAnalysis-Python-Demo/blob/master/SentimentAnalysis.ipynb)

- 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 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://githubtocolab.com/KwokHing/SentimentAnalysis-Python-Demo/blob/master/SentimentAnalysis_RNN.ipynb)

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 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://githubtocolab.com/KwokHing/SentimentAnalysis-Python-Demo/blob/master/SentimentAnalysis_BERT.ipynb)

The State-of-the-Art transformer model performs slightly better at [82%] accuracy