Bitermplus implements Biterm topic model for short texts introduced by Xiaohui Yan, Jiafeng Guo, Yanyan Lan, and Xueqi Cheng. Actually, it is a cythonized version of BTM. This package is also capable of computing perplexity, semantic coherence, and entropy metrics.

If you find this package useful, please consider donating any amount of money. This will help me spend more time on supporting open-source software.

• cython
• numpy
• pandas
• scipy
• scikit-learn
• tqdm

Be sure to install Python headers if they are not included in your Python installation. For example, in Ubuntu it can be done using this command (where x is Python minor version number):

sudo apt-get install python3.x-dev

Apart from that, there should be no issues with installing bitermplus under these OSes. You can install the package directly from PyPi:

pip install bitermplus

Or from this repo:

pip install git+https://github.com/maximtrp/bitermplus.git

First, you need to install XCode CLT and Homebrew. Then, install libomp using brew:

xcode-select --install
brew install libomp
pip3 install bitermplus

If you have the following issue with libomp (fatal error: 'omp.h' file not found), run brew info libomp in the console:

brew info libomp

You should see the following output:

libomp: stable 15.0.5 (bottled) [keg-only]
LLVM's OpenMP runtime library
https://openmp.llvm.org/
/opt/homebrew/Cellar/libomp/15.0.5 (7 files, 1.6MB)
Poured from bottle on 2022-11-19 at 12:16:49
From: https://github.com/Homebrew/homebrew-core/blob/HEAD/Formula/libomp.rb
License: MIT
==> Dependencies
Build: cmake ✘, lit ✘
==> Caveats
libomp is keg-only, which means it was not symlinked into /opt/homebrew,
because it can override GCC headers and result in broken builds.

For compilers to find libomp you may need to set:
export LDFLAGS="-L/opt/homebrew/opt/libomp/lib"
export CPPFLAGS="-I/opt/homebrew/opt/libomp/include"

==> Analytics
install: 192,197 (30 days), 373,389 (90 days), 1,285,192 (365 days)
install-on-request: 24,388 (30 days), 48,013 (90 days), 164,666 (365 days)
build-error: 0 (30 days)

Export LDFLAGS and CPPFLAGS as suggested in brew output:

export LDFLAGS="-L/opt/homebrew/opt/libomp/lib"
export CPPFLAGS="-I/opt/homebrew/opt/libomp/include"

import bitermplus as btm
import numpy as np
import pandas as pd

# IMPORTING DATA
df = pd.read_csv(
    'dataset/SearchSnippets.txt.gz', header=None, names=['texts'])
texts = df['texts'].str.strip().tolist()

# PREPROCESSING
# Obtaining terms frequency in a sparse matrix and corpus vocabulary
X, vocabulary, vocab_dict = btm.get_words_freqs(texts)
tf = np.array(X.sum(axis=0)).ravel()
# Vectorizing documents
docs_vec = btm.get_vectorized_docs(texts, vocabulary)
docs_lens = list(map(len, docs_vec))
# Generating biterms
biterms = btm.get_biterms(docs_vec)

# INITIALIZING AND RUNNING MODEL
model = btm.BTM(
    X, vocabulary, seed=12321, T=8, M=20, alpha=50/8, beta=0.01)
model.fit(biterms, iterations=20)
p_zd = model.transform(docs_vec)

# METRICS
perplexity = btm.perplexity(model.matrix_topics_words_, p_zd, X, 8)
coherence = btm.coherence(model.matrix_topics_words_, X, M=20)
# or
perplexity = model.perplexity_
coherence = model.coherence_

# LABELS
model.labels_
# or
btm.get_docs_top_topic(texts, model.matrix_docs_topics_)

You need to install tmplot first.

import tmplot as tmp
tmp.report(model=model, docs=texts)

There is a tutorial in documentation that covers the important steps of topic modeling (including stability measures and results visualization).