Coherence evaluation

This project contains source code to automaticaly evaluate coherence from topics discovered using LDA.

Quick start tutorial

To run the experiments you can follow the following tutorial:

make clean

You need an ElasticSearch instance, if not available, download and install elasticsearch executing:

make install_elasticsearch

Make sure you have the instance up and running.

elasticsearch-2.4.0/bin/elasticsearch -d

Install third-party libraries:

make requirements

Setup test data

make data

Run data preparation script to compute documents into bag-of-words

make data_preparation

Run LDA to discover topics

make topics

Compute coherence scores for the discovered topics

make coherence

Project Organization

├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── data           <- Scripts to download or generate data
│   │   └── make_dataset.py
│   │   └── local_persistence.py
│   │   └── create_database.sql
│   │   └── install_elasticsearch.sh
│   │
│   ├── features       <- Scripts to turn raw data into features for modeling
│   │   └── build_features.py
│   │   └── preprocessing.py
│   │
│   ├── models         <- Scripts to train models and then use trained models to make
│   │   │                 predictions
│   │   └── train_lda_model.py
│   │   └── topic_coherence.py
│   │   └── compute_topic_coherence.py
│   │
│
└── tox.ini            <- tox file with settings for running tox; see tox.testrun.org

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Project based on the cookiecutter data science project template. #cookiecutterdatascience

Related publications

Arian Pasquali, Marcela Canavarro, Ricardo Campos, and Alípio M. Jorge. 2016. Assessing topic discovery evaluation measures on Facebook publications of political activists in Brazil. In Proceedings of the Ninth International C* Conference on Computer Science & Software Engineering (C3S2E '16), Evan Desai (Ed.). ACM, New York, NY, USA, 25-32. DOI: http://dx.doi.org/10.1145/2948992.2949015