textanalytics 1.1

Basic computational linguistics and natural language processing in Python

text_analytics

Basic computational linguistics and natural language processing in Python.

pip install textanalytics

pip install git+https://github.com/jonathandunn/text_analytics.git

This package provides code to support introductory courses in computational linguistics or natural language processing. These courses are available free on edX:

Introduction to Text Analytics and Natural Language Processing with Python

Visualizing Text Analytics and Natural Language Processing with Python

Usage

from text_analytics import TextAnalytics

ai = TextAnalytics()

Getting features

style, vocab_size = ai.get_features(df, features="style")

style = Function word n-grams

sentiment = Positive and negative words

content = Top content words with TD-IDF weighting, PMI for finding phrases, no stop words

constructions = A bag-of-constructions syntactic representation

Using a classifier

ai.shallow_classification(df, label, features="style", cv=False, classifier='svm')

ai.mlp(df, label, features="style", validation_set=False, test_size=0.10)

Unsupervised methods

Topic Models

ai.train_lda(df, n_topics, min_count)
    
topic_df = ai.use_lda(df, labels="Author")

Vector Semantics

ai.train_word2vec(file, min_count, workers)

Document and Word Clusters

cluster_df = ai.cluster(x, y=None, k)

*Nearest document searches

 y_sample, y_closest = ai.linguistic_distance(x, y, sample=1, n=3)

Corpus Descriptions

PMI-based Phrases

ai.fit_phrases(df)

Delta P-based Phrases

association_df = ai.get_association(df, min_count = 1, save_phraser = True)

Basic word frequencies

vocab = ai._get_vocab_list(df, min_count, return_freq = True)

Corpus Comparisons

similarity = ai.get_corpus_similarity(df1, df2)