cntext 1.9.0

Chinese text analysis library, which can perform word frequency statistics, dictionary expansion, sentiment analysis, similarity, readability, co-occurrence analysis, social calculation (attitude, prejudice, culture) on texts

Table of Contents

• Installation
• QuickStart
• 1. Basic
  • 1.1 readability
  • 1.2 term_freq(text, lang)
  • 1.3 dict_pkl_list
  • 1.4 load_pkl_dict
  • 1.5 sentiment
  • 1.6 sentiment_by_valence()
• 2. dictionary
  • 2.1 SoPmi
  • 2.2 W2VModels
  • Note
  • 2.3 co_occurrence_matrix
  • 2.4 Glove
• 3. similarity
• 4. Text2Mind
  • 4.1 tm.sematic_distance(words, c_words1, c_words2)
  • 4.2 tm.sematic_projection(words, c_words1, c_words2)
• Citation
  • apalike
  • bibtex
  • endnote

中文文档

中文博客

cntext is a text analysis package that provides traditional text analysis methods, such as word count, readability, document similarity, sentiment analysis, etc. It has built-in multiple Chinese and English sentiment dictionaries. Supporting word embedding models training and usage, cntext provides semantic distance and semantic projection now.

• github repo https://github.com/hidadeng/cntext

• pypi link https://pypi.org/project/cntext/

By the day of 2023-11-20, the cumulative download volume of cntext reached 36581

Installation

pip install cntext --upgrade
pip install numpy==1.24.2
pip install gensim==4.2.0
pip install scikit-learn==1.1.2

QuickStart

import cntext as ct

help(ct)

Run

Help on package cntext:

NAME
    cntext

PACKAGE CONTENTS
    bias
    dictionary
    similarity
    stats

1. Basic

Currently, the built-in functions of stats.py are:

• readability() the readability of text, support Chinese and English
• term_freq() word count
• dict_pkl_list() get the list of built-in dictionaries (pkl format) in cntext
• load_pkl_dict() load the pkl dictionary file
• sentiment() sentiment analysis
• sentiment_by_valence() valence sentiment analysis

import cntext as ct

text = 'What a sunny day!'


diction = {'Pos': ['sunny', 'good'],
           'Neg': ['bad', 'terrible'],
           'Adv': ['very']}

ct.sentiment(text=text,
             diction=diction,
             lang='english')

Run

{'Pos_num': 1,
 'Neg_num': 0,
 'Adv_num': 0,
 'stopword_num': 1,
 'word_num': 5,
 'sentence_num': 1}

1.1 readability

The larger the indicator, the higher the complexity of the article and the worse the readability.

readability(text, lang='chinese')

• text: text string
• lang: "chinese" or "english"，default is "chinese"

import cntext as ct

text = 'Committed to publishing quality research software with zero article processing charges or subscription fees.'

ct.readability(text=text, 
               lang='english')

Run

{'readability': 19.982}

1.2 term_freq(text, lang)

Word count statistics function, return Counter type.

import cntext as ct

text = 'Committed to publishing quality research software with zero article processing charges or subscription fees.'

ct.term_freq(text=text, lang='english')

Run

Counter({'committed': 1, 
         'publishing': 1, 
         'quality': 1, 
         'research': 1, 
         'software': 1, 
         'zero': 1, 
         'article': 1, 
         'processing': 1, 
         'charges': 1, 
         'subscription': 1, 
         'fees.': 1})

1.3 dict_pkl_list

get the list of built-in dictionaries (pkl format) in cntext

import cntext as ct

ct.dict_pkl_list()

Run

['DUTIR.pkl',
 'HOWNET.pkl',
 'sentiws.pkl',
 'Chinese_Digitalization.pkl',
 'ChineseFinancialFormalUnformalSentiment.pkl',
 'Concreteness.pkl',
 'ANEW.pkl',
 'LSD2015.pkl',
 'NRC.pkl',
 'geninqposneg.pkl',
 'HuLiu.pkl',
 'AFINN.pkl',
 'ChineseEmoBank.pkl',
 'ADV_CONJ.pkl',
 'Loughran_McDonald_Financial_Sentiment.pkl',
 'Chinese_Loughran_McDonald_Financial_Sentiment.pkl',
 'STOPWORDS.pkl']

We list 12 pkl dictionary here, some of English dictionary listed below are organized from quanteda.sentiment

pkl文件	词典	语言	功能
ChineseEmoBank.pkl	Chinese Sentiment Dictionary, includes 「valence」「arousal」. In cntext, we only take Chinese valence-arousal words (CVAW, single word) into account, ignore CVAP, CVAS, CVAT.	Chinese	valence, arousal
DUTIR.pkl	DUTIR	Chinese	Seven categories of emotions: 哀, 好, 惊, 惧, 乐, 怒, 恶
HOWNET.pkl	Hownet	Chinese	Positive、Negative
SentiWS.pkl	SentimentWortschatz (SentiWS)	German	Positive、Negative；
ChineseFinancialFormalUnformalSentiment.pkl	Chinese finance dictionary, contains formal、unformal、positive、negative	Chinese	formal-pos、 formal-neg； unformal-pos、 unformal-neg
ANEW.pkl	Affective Norms for English Words (ANEW)	English
LSD2015.pkl	Lexicoder Sentiment Dictionary (2015)	English	Positive、Negative
NRC.pkl	NRC Word-Emotion Association Lexicon	English	fine-grained sentiment words;
HuLiu.pkl	Hu&Liu (2004)	English	Positive、Negative
AFINN.pkl	Affective Norms for English Words	English
ADV_CONJ.pkl	adverbial & conjunction	Chinese
STOPWORDS.pkl		English&Chinese	stopwordlist
Concreteness.pkl	Brysbaert, M., Warriner, A. B., & Kuperman, V. (2014). Concreteness ratings for 40 thousand generally known English word lemmas. Behavior Research Methods, 46, 904–911	English	word & concreateness score
Chinese_Loughran_McDonald_Financial_Sentiment.pkl	曾庆生, 周波, 张程, and 陈信元. "年报语调与内部人交易: 表里如一还是口是心非?." 管理世界 34, no. 09 (2018): 143-160.	Chinese	正面、负面词
Chinese_Digitalization.pkl	吴非,胡慧芷,林慧妍,任晓怡. 企业数字化转型与资本市场表现——来自股票流动性的经验证据[J]. 管理世界,2021,37(07):130-144+10.	中文	基于这篇论文，构建了中文数字化词典，含人工智能技术、大数据技术、云计算技术、区块链技术、数字技术应用等关键词列表。
Loughran_McDonald_Financial_Sentiment.pkl	Loughran, Tim, and Bill McDonald. "When is a liability not a liability? Textual analysis, dictionaries, and 10‐Ks." The Journal of finance 66, no. 1 (2011): 35-65.	English	Positive and Negative emotion words in the financial field。 Besides, in version of 2018, author add Uncertainty, Litigious, StrongModal, WeakModal, Constraining
Chinese_FLS.pkl	许帅,邵帅,何贤杰.业绩说明会前瞻性信息对分析师盈余预测准确性的影响——信口雌黄还是言而有征[J].中国管理科学:1-15.	中文	前瞻性词典集，含174个词语

1.4 load_pkl_dict

load the pkl dictionary file and return dict type data.

import cntext as ct

print(ct.__version__)
# load the pkl dictionary file
print(ct.load_pkl_dict('NRC.pkl'))

Run

1.8.0

{'NRC': {'anger': ['abandoned', 'abandonment', 'abhor', 'abhorrent', ...],
         'anticipation': ['accompaniment','achievement','acquiring', ...],
         'disgust': ['abject', 'abortion', 'abundance', 'abuse', ...],
         'fear': ['anxiety', 'anxious', 'apache', 'appalling', ...],
         ......
 
 'Desc': 'NRC Word-Emotion Association Lexicon', 
 'Referer': 'Mohammad, Saif M., and Peter D. Turney. "Nrc emotion lexicon." National Research Council, Canada 2 (2013).'
         }

1.5 sentiment

sentiment(text, diction, lang='chinese')

Calculate the occurrences of each emotional category words in text; The complex influence of adverbs and negative words on emotion is not considered.

• text: text string
• diction: emotion dictionary data, support diy or built-in dicitonary
• lang: "chinese" or "english"，default is "chinese"

We can use built-in dicitonary in cntext, such as NRC.pkl

import cntext as ct

text = 'What a happy day!'

ct.sentiment(text=text,
             diction=ct.load_pkl_dict('NRC.pkl')['NRC'],
             lang='english')

Run

{'anger_num': 0,
 'anticipation_num': 1,
 'disgust_num': 0,
 'fear_num': 0,
 'joy_num': 1,
 'negative_num': 0,
 'positive_num': 1,
 'sadness_num': 0,
 'surprise_num': 0,
 'trust_num': 1,
 'stopword_num': 1,
 'word_num': 5,
 'sentence_num': 1}

We can also use DIY dicitonary, just like

import cntext as ct

text = 'What a happy day!'

diction = {'Pos': ['happy', 'good'],
           'Neg': ['bad', 'terrible'],
           'Adv': ['very']}

ct.sentiment(text=text,
             diction=diction,
             lang='english')

Run

{'Pos_num': 1,
 'Neg_num': 0,
 'Adv_num': 0,
 'stopword_num': 1,
 'word_num': 5,
 'sentence_num': 1}

1.6 sentiment_by_valence()

sentiment_by_valence(text, diction, lang='english')

Calculate the occurrences of each sentiment category words in text; The complex influence of intensity adverbs and negative words on emotion is not considered.

• text: text sring
• diction: sentiment dictionary with valence.；
• lang: "chinese" or "english"; default language="english"

Here we want to study the concreteness of text. The concreteness.pkl that comes from Brysbaert2014.

Brysbaert, M., Warriner, A. B., & Kuperman, V. (2014). Concreteness ratings for 40 thousand generally known English word lemmas. Behavior Research Methods, 46, 904–911

import cntext as ct

# load the concreteness.pkl dictionary file;  cntext version >=1.7.1
concreteness_df = ct.load_pkl_dict('concreteness.pkl')['concreteness']
concreteness_df.head()

Run

	word	valence
0	roadsweeper	4.85
1	traindriver	4.54
2	tush	4.45
3	hairdress	3.93
4	pharmaceutics	3.77

reply = "I'll go look for that"

score=ct.sentiment_by_valence(text=reply, 
                              diction=concreteness_df, 
                              lang='english')
score

Run

1.85

employee_replys = ["I'll go look for that",
                   "I'll go search for that",
                   "I'll go search for that top",
                   "I'll go search for that t-shirt",
                   "I'll go look for that t-shirt in grey",
                   "I'll go search for that t-shirt in grey"]

for idx, reply in enumerate(employee_replys):
    score=ct.sentiment_by_valence(text=reply, 
                                  diction=concreteness_df, 
                                  lang='english')
    
    template = "Concreteness Score: {score:.2f} | Example-{idx}: {exmaple}"
    print(template.format(score=score, 
                          idx=idx, 
                          exmaple=reply))
    
ct.sentiment_by_valence(text=text, diction=concreteness_df, lang='english')

Run

Concreteness Score: 1.55 | Example-0: I'll go look for that
Concreteness Score: 1.55 | Example-1: I'll go search for that
Concreteness Score: 1.89 | Example-2: I'll go search for that top
Concreteness Score: 2.04 | Example-3: I'll go search for that t-shirt
Concreteness Score: 2.37 | Example-4: I'll go look for that t-shirt in grey
Concreteness Score: 2.37 | Example-5: I'll go search for that t-shirt in grey

2. dictionary

This module is used to build or expand the vocabulary (dictionary), including

• SoPmi Co-occurrence algorithm to extend vocabulary (dictionary), Only support chinese
• W2VModels using word2vec to extend vocabulary (dictionary), support english & chinese

2.1 SoPmi

import cntext as ct
import os

sopmier = ct.SoPmi(cwd=os.getcwd(),
                   #raw corpus data，txt file.only support chinese data now.
                   input_txt_file='data/sopmi_corpus.txt', 
                   #muanually selected seed words
                   seedword_txt_file='data/sopmi_seed_words.txt', #人工标注的初始种子词
                   )   

sopmier.sopmi()

Run

Step 1/4:...Preprocess   Corpus ...
Step 2/4:...Collect co-occurrency information ...
Step 3/4:...Calculate   mutual information ...
Step 4/4:...Save    candidate words ...
Finish! used 44.49 s

2.2 W2VModels

In particular, note that the code needs to set the lang parameter

import cntext as ct
import os

#init W2VModels, corpus data w2v_corpus.txt
model = ct.W2VModels(cwd=os.getcwd(), lang='english')  
model.train(input_txt_file='data/w2v_corpus.txt')


#According to the seed word, filter out the top 100 words that are most similar to each category words
model.find(seedword_txt_file='data/w2v_seeds/integrity.txt', 
           topn=100)
model.find(seedword_txt_file='data/w2v_seeds/innovation.txt', 
           topn=100)
model.find(seedword_txt_file='data/w2v_seeds/quality.txt', 
           topn=100)
model.find(seedword_txt_file='data/w2v_seeds/respect.txt', 
           topn=100)
model.find(seedword_txt_file='data/w2v_seeds/teamwork.txt', 
           topn=100)

Run

Step 1/4:...Preprocess   corpus ...
Step 2/4:...Train  word2vec model
            used   174 s
Step 3/4:...Prepare similar candidates for each seed word in the word2vec model...
Step 4/4 Finish! Used 187 s
Step 3/4:...Prepare similar candidates for each seed word in the word2vec model...
Step 4/4 Finish! Used 187 s
Step 3/4:...Prepare similar candidates for each seed word in the word2vec model...
Step 4/4 Finish! Used 187 s
Step 3/4:...Prepare similar candidates for each seed word in the word2vec model...
Step 4/4 Finish! Used 187 s
Step 3/4:...Prepare similar candidates for each seed word in the word2vec model...
Step 4/4 Finish! Used 187 s

Note

When runing out the W2VModels, there will appear a file called w2v.model in the directory of output/w2v_candi_words.Note this w2v file can be used later.

from gensim.models import KeyedVectors

w2v_model = KeyedVectors.load("the path of w2v.model")
#to extract vector for word
#w2v_model.get_vector(word)
#if you need more information about the usage of w2_model, please use help function
#help(w2_model)

For example, we load the output/w2v_candi_words/w2v.model

from gensim.models import KeyedVectors

w2v_model = KeyedVectors.load('output/w2v_candi_words/w2v.model')
# find the most similar word in w2v.model
w2v_model.most_similar('innovation')

Run

[('technology', 0.689210832118988),
 ('infrastructure', 0.669672966003418),
 ('resources', 0.6695448160171509),
 ('talent', 0.6627111434936523),
 ('execution', 0.6549549102783203),
 ('marketing', 0.6533523797988892),
 ('merchandising', 0.6504817008972168),
 ('diversification', 0.6479553580284119),
 ('expertise', 0.6446896195411682),
 ('digital', 0.6326863765716553)]

#to extract vector for "innovation"
w2v_model.get_vector('innovation')

Run

array([-0.45616838, -0.7799563 ,  0.56367606, -0.8570078 ,  0.600359  ,
       -0.6588043 ,  0.31116748, -0.11956959, -0.47599426,  0.21840936,
       -0.02268819,  0.1832016 ,  0.24452794,  0.01084935, -1.4213187 ,
        0.22840202,  0.46387577,  1.198386  , -0.621511  , -0.51598716,
        0.13352732,  0.04140598, -0.23470387,  0.6402956 ,  0.20394802,
        0.10799981,  0.24908689, -1.0117126 , -2.3168423 , -0.0402851 ,
        1.6886286 ,  0.5357047 ,  0.22932841, -0.6094084 ,  0.4515793 ,
       -0.5900931 ,  1.8684244 , -0.21056202,  0.29313338, -0.221067  ,
       -0.9535679 ,  0.07325   , -0.15823542,  1.1477109 ,  0.6716076 ,
       -1.0096023 ,  0.10605699,  1.4148282 ,  0.24576302,  0.5740349 ,
        0.19984631,  0.53964925,  0.41962907,  0.41497853, -1.0322098 ,
        0.01090925,  0.54345983,  0.806317  ,  0.31737605, -0.7965337 ,
        0.9282971 , -0.8775608 , -0.26852605, -0.06743863,  0.42815775,
       -0.11774074, -0.17956367,  0.88813037, -0.46279573, -1.0841943 ,
       -0.06798118,  0.4493006 ,  0.71962464, -0.02876493,  1.0282255 ,
       -1.1993176 , -0.38734904, -0.15875885, -0.81085825, -0.07678922,
       -0.16753489,  0.14065655, -1.8609751 ,  0.03587054,  1.2792674 ,
        1.2732009 , -0.74120265, -0.98000383,  0.4521185 , -0.26387128,
        0.37045383,  0.3680011 ,  0.7197629 , -0.3570571 ,  0.8016917 ,
        0.39243212, -0.5027844 , -1.2106236 ,  0.6412354 , -0.878307  ],
      dtype=float32)

2.3 co_occurrence_matrix

generate word co-occurrence matrix

import cntext as ct

documents = ["I go to school every day by bus .",
         "i go to theatre every night by bus"]

ct.co_occurrence_matrix(documents, 
                        window_size=2, 
                        lang='english')

2.4 Glove

Build the Glove model for english corpus data. corpus file path is data/brown_corpus.txt

import cntext as ct
import os

model = ct.Glove(cwd=os.getcwd(), lang='english')
model.create_vocab(file='data/brown_corpus.txt', min_count=5)
model.cooccurrence_matrix()
model.train_embeddings(vector_size=50, max_iter=25)
model.save()

Run

Step 1/4: ...Create vocabulary for Glove.
Step 2/4: ...Create cooccurrence matrix.
Step 3/4: ...Train glove embeddings. 
             Note, this part takes a long time to run
Step 3/4: ... Finish! Use 175.98 s

The generate生成的词嵌入模型文件位于output/Glove内

3. similarity

Four text similarity functions

• cosine_sim(text1, text2)
• jaccard_sim(text1, text2)
• minedit_sim(text1, text2)
• simple_sim(text1, text2)

Algorithm implementation reference from Cohen, Lauren, Christopher Malloy, and Quoc Nguyen. Lazy prices. No. w25084. National Bureau of Economic Research, 2018.

import cntext as ct 


text1 = 'Programming is fun!'
text2 = 'Programming is interesting!'

print(ct.cosine_sim(text1, text2))
print(ct.jaccard_sim(text1, text2))
print(ct.minedit_sim(text1, text2))
print(ct.simple_sim(text1, text2))

Run

0.67
0.50
1.00
0.90

4. Text2Mind

Word embeddings contain human cognitive information.

• tm.sematic_distance(words, c_words1, c_words2)
• tm.sematic_projection(words, c_words1, c_words2)

4.1 tm.sematic_distance(words, c_words1, c_words2)

Calculate the two semantic distance， and return the difference between the two.

• words concept words, words = ['program', 'software', 'computer']
• c_words1 concept words1, c_words1 = ["man", "he", "him"]
• c_words2 concept words2, c_words2 = ["woman", "she", "her"]

For example,

male_concept = ['male', 'man', 'he', 'him']

female_concept = ['female', 'woman', 'she', 'her']

software_engineer_concept  = ['engineer',  'programming',  'software']

d1 = distance(male_concept,  software_engineer_concept)

d2 = distance(female_concept,  software_engineer_concept)

If d1-d2<0，it means in semantic space, between man and woman, software_engineer_concept is more closer to male_concept。

In other words, there is a stereotype (bias) of women for software engineers in this corpus.

download glove_w2v.6B.100d.txt from google Driver

import cntext as ct

#Note: this is a word2vec format model
tm = ct.Text2Mind(w2v_model_path='glove_w2v.6B.100d.txt')

engineer = ['program', 'software', 'computer']
mans =  ["man", "he", "him"]
womans = ["woman", "she", "her"]


tm.sematic_distance(words=animals, 
                    c_words1=mans, 
                    c_words2=womans)

Run

-0.38

-0.38 means in semantic space, engineer is closer to man, other than woman.

4.2 tm.sematic_projection(words, c_words1, c_words2)

To explain the semantic projection of the word vector model, I use the picture from a Nature paper in 2022[@Grand2022SemanticPR]. Regarding the names of animals, human cognition information about animal size is hidden in the corpus text. By projecting the meaning of LARGE WORDS and SMALL WORDS with the vectors of different animals, the projection of the animal on the size vector(just like the red line in the bellow picture) is obtained, so the size of the animal can be compared by calculation.

Calculate the projected length of each word vector in the concept vector.Note that the calculation result reflects the direction of concept.Greater than 0 means semantically closer to c_words2.

Grand, G., Blank, I.A., Pereira, F. and Fedorenko, E., 2022. Semantic projection recovers rich human knowledge of multiple object features from word embeddings. Nature Human Behaviour, pp.1-13.

For example, in the corpus, perhaps show that our human beings have different size memory(perception) about animals.

animals = ['mouse', 'cat', 'horse',  'pig', 'whale']
small_words = ["small", "little", "tiny"]
large_words = ["large", "big", "huge"]

tm.sematic_projection(words=animals, 
                      c_words1=small_words, 
                      c_words2=large_words)

Run

[('mouse', -1.68),
 ('cat', -0.92),
 ('pig', -0.46),
 ('whale', -0.24),
 ('horse', 0.4)]

Regarding the perception of size, humans have implied in the text that mice are smaller and horses are larger.

Citation

If you use cntext in your research or in your project, please cite:

apalike

Deng X., Nan P. (2022). cntext: a Python tool for text mining (version 1.7.9). DOI: 10.5281/zenodo.7063523 URL: https://github.com/hiDaDeng/cntext

bibtex

@misc{YourReferenceHere,
author = {Deng, Xudong and Nan, Peng},
doi = {10.5281/zenodo.7063523},
month = {9},
title = {cntext: a Python tool for text mining},
url = {https://github.com/hiDaDeng/cntext},
year = {2022}
}

endnote

%0 Generic
%A Deng, Xudong
%A Nan, Peng
%D 2022
%K text mining
%K text analysi
%K social science
%K management science
%K semantic analysis
%R 10.5281/zenodo.7063523
%T cntext: a Python tool for text mining
%U https://github.com/hiDaDeng/cntext