release version 0.9

README.rst

@@ -35,7 +35,7 @@ Features
 -  Stemmer
 -  **Summarization**, using skip-thought state-of-art to give precise
    summarization.
--  Topic Modelling
+-  **Topic Modelling**, provide LDA2Vec, LDA, NMF and LSA interface for easy topic modelling.
 -  **Topic and Influencers Analysis**, using deep and machine learning
    models to understand topics and Influencers similarity in sentences.
 -  **Toxicity Analysis**, from BERT, Fast-Text, Dynamic-Memory Network,

build-dependencies.sh

@@ -1,4 +1,4 @@
-pip3 install xgboost==0.80 tqdm nltk unidecode numpy scipy python-levenshtein tensorflow
+pip3 install xgboost==0.80 tqdm unidecode numpy scipy python-levenshtein tensorflow pyldavis
 pip3 install sklearn_crfsuite sklearn scikit-learn==0.19.1 requests fuzzywuzzy
 pip3 install pandas toolz
 pip3 install PySastrawi

docs/Cluster.rst

@@ -0,0 +1,9 @@
+Text Clustering
+====================
+
+.. note::
+
+    This tutorial is available as an IPython notebook
+    `here <https://github.com/DevconX/Malaya/tree/master/example/text-clustering>`_.
+
+.. include:: text-clustering-generator.rst

docs/README.rst

@@ -35,7 +35,7 @@ Features
 -  Stemmer
 -  **Summarization**, using skip-thought state-of-art to give precise
    summarization.
--  Topic Modelling
+-  **Topic Modelling**, provide LDA2Vec, LDA, NMF and LSA interface for easy topic modelling.
 -  **Topic and Influencers Analysis**, using deep and machine learning
    models to understand topics and Influencers similarity in sentences.
 -  **Toxicity Analysis**, from BERT, Fast-Text, Dynamic-Memory Network,

docs/conf.py

@@ -133,6 +133,7 @@ def __getattr__(cls, name):
     'load-*.rst',
     '*.ipynb',
     'references.rst',
+    'text-clustering-generator.rst',
 ]
 
 # The name of the Pygments (syntax highlighting) style to use.

docs/index.rst

@@ -32,5 +32,6 @@ Contents:
    Topics
    Toxic
    Word2vec
+   Cluster
    Api
    Reference