Package: eKoNLPy
eKoNLPy: Korean NLP Python Library for Economic AnalysiseKoNLPyis a Korean Natural Language Processing (NLP) Python library specifically designed for economic analysis. It extends the functionality of theMecabtagger from KoNLPy to improve the handling of economic terms, financial institutions, and company names, classifying them as single nouns. Additionally, it incorporates sentiment analysis features to determine the tone of monetary policy statements, such as Hawkish or Dovish.NoteFrom version 2.0.0, eKoNLPy integrates the extended tagger with the original tagger. If you want to use the original tagger, please setuse_original_tagger=Truewhen you create the instance ofMecabclass. Additionally, theMecabclass can be directly imported fromekonlpymodule. The default input text parameter ofMecab.pos()is changed fromphrasetotextto be consistent with the original tagger.NoteeKoNLPy is built on thefugashiandmecab-ko-diclibraries. For more information on using theMecabtagger, please refer to thefugashi documentation. As eKoNLPy no longer relies on theKoNLPylibrary, Java is not required for its use. This makes eKoNLPy compatible with Windows, Linux, and Mac OS, without the need for Java installation. You can also use eKoNLPy on Google Colab.If you wish to tokenize general Korean text with eKoNLPy, you do not need to install theKoNLPylibrary. Instead, utilize the sameekonlpy.Mecabclass with theuse_original_tagger=Trueoption.However, if you plan to use theKorean Sentiment Analyzer (KSA), which employs theKkmamorpheme analyzer, you will need to install theKoNLPylibrary.InstallationTo install eKoNLPy, run the following command:pipinstallekonlpyUsagePart of Speech TaggingTo use the part of speech tagging feature, inputMecab.pos(phrase)just like KoNLPy. First, the input is processed using KoNLPy's Mecab morpheme analyzer. Then, if a combination of consecutive tokens matches a term in the user dictionary, the phrase is separated into compound nouns.fromekonlpyimportMecabmecab=Mecab()mecab.pos('금통위는 따라서 물가안정과 병행, 경기상황에 유의하는 금리정책을 펼쳐나가기로 했다고 밝혔다.')[('금', 'MAJ'), ('통', 'MAG'), ('위', 'NNG'), ('는', 'JX'), ('따라서', 'MAJ'), ('물가', 'NNG'), ('안정', 'NNG'), ('과', 'JC'), ('병행', 'NNG'), (',', 'SC'), ('경기', 'NNG'), ('상황', 'NNG'), ('에', 'JKB'), ('유의', 'NNG'), ('하', 'XSV'), ('는', 'ETM'), ('금리', 'NNG'), ('정책', 'NNG'), ('을', 'JKO'), ('펼쳐', 'VV+EC'), ('나가', 'VX'), ('기', 'ETN'), ('로', 'JKB'), ('했', 'VV+EP'), ('다고', 'EC'), ('밝혔', 'VV+EP'), ('다', 'EF'), ('.', 'SF')]You can also use the Command Line Interface (CLI) to perform part of speech tagging:ekonlpy--input"안녕하세요"[('안녕', 'NNG'), ('하', 'XSV'), ('세요', 'EP')]cf. Original Mecab POS Tagging (fugashi)fromekonlpyimportMecabmecab=Mecab(use_original_tagger=True)# set use_original_tagger=Truemecab.pos('금통위는 따라서 물가안정과 병행, 경기상황에 유의하는 금리정책을 펼쳐나가기로 했다고 밝혔다.')[('금', 'MAJ'), ('통', 'MAG'), ('위', 'NNG'), ('는', 'JX'), ('따라서', 'MAJ'), ('물가', 'NNG'), ('안정', 'NNG'), ('과', 'JC'), ('병행', 'NNG'), (',', 'SC'), ('경기', 'NNG'), ('상황', 'NNG'), ('에', 'JKB'), ('유의', 'NNG'), ('하', 'XSV'), ('는', 'ETM'), ('금리', 'NNG'), ('정책', 'NNG'), ('을', 'JKO'), ('펼쳐', 'VV+EC'), ('나가', 'VX'), ('기', 'ETN'), ('로', 'JKB'), ('했', 'VV+EP'), ('다고', 'EC'), ('밝혔', 'VV+EP'), ('다', 'EF'), ('.', 'SF')]Lemmatization and SynonymsTo enhance the accuracy of sentiment analysis, eKoNLPy offers lemmatization and synonym handling features.Adding Words to DictionaryYou can add words to the dictionary in theekonlpy.tagmodule's Mecab class, either as a string or a list of strings, using theadd_dictionarymethod.fromekonlpy.tagimportMecabmecab=Mecab()mecab.add_dictionary('금통위','NNG')Sentiment AnalysisKorean Monetary Policy Dictionary (MPKO)To perform sentiment analysis using the Korean Monetary Policy dictionary, create an instance of theMPKOclass inekonlpy.sentiment:fromekonlpy.sentimentimportMPKOmpko=MPKO(kind=1)tokens=mpko.tokenize(text)score=mpko.get_score(tokens)Thekindparameter in theMPKOclass is used to select a lexicon file:0: A lexicon file generated using a Naive-Bayes classifier with 5-gram tokens as features and changes of call rates as positive/negative labels.1: A lexicon file generated by polarity induction and seed propagation method with 5-gram tokens.Korean Monetary Policy Classifier (MPCK)To use a classifier for monetary policy sentiment analysis, utilize theMPCKclass fromekonlpy.sentiment:fromekonlpy.sentimentimportMPCKmpck=MPCK()tokens=mpck.tokenize(text)ngrams=mpck.ngramize(tokens)score=mpck.classify(tokens+ngrams,intensity_cutoff=1.5)You can set theintensity_cutoffparameter to adjust the intensity for classifying low-accuracy sentences as neutral (default: 1.3).Korean Sentiment Analyzer (KSA)For general Korean sentiment analysis, use theKSAclass. The morpheme analyzer used in this class isKkmadeveloped by Seoul National University's IDS Lab. The sentiment dictionary is also from the same lab (reference:http://kkma.snu.ac.kr/).fromekonlpy.sentimentimportKSAksa=KSA()tokens=ksa.tokenize(text)score=ksa.get_score(tokens)Harvard IV-4 DictionaryFor general English sentiment analysis, use the Harvard IV-4 dictionary:fromekonlpy.sentimentimportHIV4hiv=HIV4()tokens=hiv.tokenize(text)score=hiv.get_score(tokens)Loughran and McDonald DictionaryFor sentiment analysis in the financial domain, use the Loughran and McDonald dictionary:fromekonlpy.sentimentimportLMlm=LM()tokens=lm.tokenize(text)score=lm.get_score(tokens)ChangelogSee theCHANGELOGfor more information.ContributingContributions are welcome! Please see thecontributing guidelinesfor more information.LicenseeKoNLPy is an open-source software library for Korean Natural Language Processing (NLP), specifically designed for economic analysis. The library is released under theMIT License, allowing developers and researchers to use, modify, and distribute the software as they see fit.CitationIf you use eKoNLPy in your work or research, please cite the following sources:Lee, Young Joon, eKoNLPy: A Korean NLP Python Library for Economic Analysis, 2018. Available at:https://github.com/entelecheia/eKoNLPy.Lee, Young Joon, Soohyon Kim, and Ki Young Park. "Deciphering Monetary Policy Board Minutes with Text Mining: The Case of South Korea." Korean Economic Review 35 (2019): 471-511.You can also use the following BibTeX entry for citation:@misc{lee2018ekonlpy,author={Lee, Young Joon},year={2018},title={{eKoNLPy: A Korean NLP Python Library for Economic Analysis}},note={\url{https://github.com/entelecheia/eKoNLPy}}}By citing eKoNLPy in your work, you acknowledge the efforts and contributions of its creators and help promote further development and research in the field of Korean NLP for economic analysis.