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cntext-1.8.4


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توضیحات

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
ویژگی مقدار
سیستم عامل -
نام فایل cntext-1.8.4
نام cntext
نسخه کتابخانه 1.8.4
نگهدارنده []
ایمیل نگهدارنده []
نویسنده 大邓
ایمیل نویسنده thunderhit@qq.com
آدرس صفحه اصلی https://github.com/hidadeng/cntext
آدرس اینترنتی https://pypi.org/project/cntext/
مجوز MIT
[![DOI](https://zenodo.org/badge/487297608.svg)](https://zenodo.org/badge/latestdoi/487297608) <!-- START doctoc generated TOC please keep comment here to allow auto update --> <!-- DON'T EDIT THIS SECTION, INSTEAD RE-RUN doctoc TO UPDATE --> **Table of Contents** - [Installation](#installation) - [QuickStart](#quickstart) - [1. Basic](#1-basic) - [1.1 readability](#11--readability) - [1.2 term_freq(text, lang)](#12--term_freqtext-lang) - [1.3 dict_pkl_list](#13-dict_pkl_list) - [1.4 load_pkl_dict](#14-load_pkl_dict) - [1.5 sentiment](#15-sentiment) - [1.6 sentiment_by_valence()](#16-sentiment_by_valence) - [2. dictionary](#2-dictionary) - [2.1 SoPmi](#21-sopmi) - [2.2 W2VModels](#22-w2vmodels) - [Note](#note) - [2.3 co_occurrence_matrix](#23-co_occurrence_matrix) - [2.4 Glove](#24--glove) - [3. similarity](#3-similarity) - [4. Text2Mind](#4-text2mind) - [4.1 tm.sematic_distance(words, c_words1, c_words2)](#41-tmsematic_distancewords-c_words1-c_words2) - [4.2 tm.sematic_projection(words, c_words1, c_words2)](#42-tmsematic_projectionwords-c_words1-c_words2) - [Citation](#citation) - [apalike](#apalike) - [bibtex](#bibtex) - [endnote](#endnote) <!-- END doctoc generated TOC please keep comment here to allow auto update --> ![](img/logo.png) [中文文档](chinese_readme.md) [中文博客](https://hidadeng.github.io/blog/) **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) ``https://github.com/hidadeng/cntext`` - [pypi link](https://pypi.org/project/cntext/) ``https://pypi.org/project/cntext/`` <br> ## Installation ``` pip install cntext ``` <br> ## QuickStart ```python import cntext as ct help(ct) ``` Run ``` Help on package cntext: NAME cntext PACKAGE CONTENTS bias dictionary similarity stats ``` <br> ## 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 ```python 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} ``` <br> ### 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" ```python 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} ``` <br> ### 1.2 term_freq(text, lang) Word count statistics function, return Counter type. ```python 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}) ``` <br> ### 1.3 dict_pkl_list get the list of built-in dictionaries (pkl format) in cntext ```python 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](https://github.com/quanteda/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;<br> | | ChineseFinancialFormalUnformalSentiment.pkl | Chinese finance dictionary, contains formal、unformal、positive、negative | Chinese | formal-pos、<br>formal-neg;<br>unformal-pos、<br>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`` | <br> ### 1.4 load_pkl_dict load the pkl dictionary file and return dict type data. ```python 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).' } ``` <br> ### 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 ```python 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 ```python 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} ``` <br> ### 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 ```python 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 | <br> ```python reply = "I'll go look for that" score=ct.sentiment_by_valence(text=reply, diction=concreteness_df, lang='english') score ``` Run ``` 1.85 ``` <br> ```python 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 ``` <br><br> ## 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 ```python 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 ``` <br> ### 2.2 W2VModels **In particular, note that the code needs to set the lang parameter** ```python 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 ``` <br> ### 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. ```python 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`` ```python 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)] ``` <br> ```python #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) ``` <br><br> ### 2.3 co_occurrence_matrix generate word co-occurrence matrix ```python 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') ``` ![](img/co_occurrence1.png) <br><br> ### 2.4 Glove Build the Glove model for english corpus data. corpus file path is ``data/brown_corpus.txt`` ```python 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 <br><br> ## 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.`` <br> ```python 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 ``` <br><br> ## 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](https://drive.google.com/file/d/1tuQB9PDx42z67ScEQrg650aDTYPz-elJ/view?usp=sharing) ```python 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. <br> ### 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. ![](img/Nature_Semantic_projection_recovering_human_knowledge_of.png) For example, in the corpus, perhaps show that our human beings have different size memory(perception) about animals. ```python 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. <br><br> ## 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 ```


نیازمندی

مقدار نام
- jieba
==1.20.0 numpy
- mittens
- scikit-learn
- numpy
- matplotlib
- pyecharts
>=4.2.0 gensim
- nltk
>=1.3.5 pandas


زبان مورد نیاز

مقدار نام
>=3.5 Python


نحوه نصب


نصب پکیج whl cntext-1.8.4:

    pip install cntext-1.8.4.whl


نصب پکیج tar.gz cntext-1.8.4:

    pip install cntext-1.8.4.tar.gz