https://github.com/khuyentran1401/author-profiling
https://github.com/khuyentran1401/author-profiling
bert nlp sklearn text-classification text-processing tf-idf tsne twitter word2vec
Last synced: 7 days ago
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- Host: GitHub
- URL: https://github.com/khuyentran1401/author-profiling
- Owner: khuyentran1401
- Created: 2020-03-06T02:33:49.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2020-04-05T17:02:27.000Z (over 5 years ago)
- Last Synced: 2025-03-27T03:51:19.231Z (7 months ago)
- Topics: bert, nlp, sklearn, text-classification, text-processing, tf-idf, tsne, twitter, word2vec
- Language: Jupyter Notebook
- Size: 4.03 MB
- Stars: 4
- Watchers: 2
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Task
Identity gender and language variety in Twitter in English. In specific, we want to classify between **male** and **female** and their locations including **Australia, Canada, Great Britain, Ireland, New Zealand, United States**
# Dataset
Dataset is provided by [Pan 2017](https://pan.webis.de/clef17/pan17-web/author-profiling.html)
# Methods
* Get data from .xml files
* Preprocessing with NLTK
* Filter stopwords, punctuations, and lowercase
* Tokenize
* Stem words
* Visualization
* Visualization with TSNE (a tool to visualize high-dimensional data)
* Model training
* Support Vector Machine with variety of embeddings, including:
* Bag of words from scratch
* Tf-Idf Vectorizer
* Word2Vec
* Combination of Tf-Idf and Word2Vec
* BERT
* Neural Network with BERT and PyTorch
# Result
Using Tf-Idf achieves the highes f1-score. The result is shown in the table below:
**Gender**: _female_: 0, _male_: 1
. | precision | recall | f1-score | support
------------ | ------------- | ------------- | ------------- | -------------
0 | 0.79 | 0.82 | 0.80 | 1200
1 | 0.81 | 0.78 | 0.79 | 1200
accuracy | _ | _ | 0.80 | 2400
macro avg | 0.80 | 0.80 | 0.80 | 2400
weighted avg | 0.80 | 0.80 | 0.80 | 2400
**Language Variety**: _australia_: 0, _canada_: 1, _great britain_: 2, _ireland_: 3, _new zealand_: 4, _United States_: 5
. | precision | recall | f1-score | support
------------ | ------------- | ------------- | ------------- | -------------
0 | 0.85 | 0.83 | 0.84 | 400
1 | 0.78 | 0.85 | 0.81 | 400
2 | 0.85 | 0.81 | 0.83 | 400
3 | 0.87 | 0.85 | 0.86 | 400
4 | 0.91 | 0.90 | 0.90 | 400
5 | 0.82 | 0.82 | 0.82 | 400
accuracy | _ | _ | 0.84 | 2400
macro avg | 0.85 | 0.84 | 0.84 | 2400
weighted avg | 0.85 | 0.84 | 0.84 | 2400