Repustate's sentiment analysis API is powered by Korean natural language processing (NLP). It analyzes Korean data natively, without the need to translate any document into English, thus, giving more accurate and meticulously useful insights.
Repustate's Korean sentiment analysis solution is made dedicatedly for the Korean-speaking world. The tool can be used to analyze data from any Korean source such as news, social media, blogs, reviews, and surveys.
The solution has its own dedicated Korean part-of-speech tagger, Korean lemmatizer, and Korean-specific sentiment models. It also understands colloquial words and industry jargon. Korean natural language processing, coupled with named entity recognition (NER), helps it identify topics and themes in the data for more granular sentiment analysis, leading to key business insights.
See Repustate's Korean sentiment analysis in action
What are the benefits of Repustate's Korean Sentiment Analytics Tool?
The Repustate Korean Sentiment Analysis solution is made specifically for Korean data. It is highly
customizable and can be calibrated to be an exact fit for your business. Listed below are the main advantages of the tool:
Obtain granular Korean sentiment analysis by aspect
Data security with on-premise deployment or through Cloud
Custom-made model that can be specific to your brand and industry domain
Multilingual sentiment analysis capability to help you scale globally fast
Why Choose Repustate Over Others?
The Repustate model has the highest accuracy in NER compared to other
tools. It is not an off-the-shelf product, but a highly personalized,
scalable solution with dedicated customer support. Here is why our clients worldwide (NA, EMEA and Asia Pacific) collaborate with us.
Premium Support: As our client, you will have a dedicated engineer assigned to your account.
Technology: We apply artificial intelligence and machine learning to Korean natural language processing for the best results.
Korean Text: We never translate. Repustate has unique speech taggers and sentiment models made just for Korean text analysis. In fact, each of our 23 languages has an ML model based on the natural language natively.
Accuracy: Our aspect-driven approach to sentiment is granular, accurate, and targeted.
Speed: Our API can process 1,000 comments per second.
Customizable: We custom-build the model to capture your most important aspects and topics.
Scale: Our API can easily scale from 1 to 10 million to 100 million documents and beyond.
Integration: The Repustate solution integrates seamlessly with your existing technology. You do not need 3rd party support for any underlying technology.
Competition: Our model is the most accurate in NER compared to the competition that includes Google, Amazon, and Microsoft.
Check out our comparison.
Deployment Flexibility: We offer both, a Cloud model, and an on-premise installation for data security, in a one-click solution.
No Hidden Fees: Our pricing includes support, updates, training, and deployment.
Iterative: Our models get more and more intelligent with training and as they process more data.
Korean Language Sentiment Models
Repustate has developed sentiment language models specific to Korean
to capture the various phrases, idioms, and expressions that help define certain sentiments. It turns unstructured
Korean data into business intelligence you can use to increase your value proposition, brand experience, and value delivery.
Take a quick tour of Repustate's Korean text analytics solution
What are the Basic Steps in Korean Sentiment Analysis?
Repustate has a massive corpus of Korean text that has been tagged manually,
as the first step towards Korean sentiment analysis. This corpus is then processed
by an ML model for high precision in aggregate sentiment scoring. The steps can be laid out as:
Collect a massive, highly varied corpus (collection of texts) of the manually tagged Korean text.
Feed this text into an ML-based algorithm to create an Korean part-of-speech tagger.
Granulate the algorithm for deeper accuracy using NER.
Extract the sentiment score for each aspect, theme, and topic.
Repustate uses Korean Named Entity Recognition (NER) with
advanced semantic search to identify brand and business entities in data. No matter how misspelled a word is,
the Korean NLP model will reproduce the name in the native script and give accurate name search,
transliteration, and identity verification measures. This gives high-accuracy ranked results, based on the
linguistic, phonetic, and specific cultural variation patterns of the names.
Should translations be used for Korean sentiment analysis?
Trying to achieve Korean sentiment analysis by using an API that uses translations can
yield incorrect results. So, the answer is, no. As your Korean sentiment analysis company,
Repustate, uses intricate Korean NLP for higher accuracy in sentiment scoring of your data.
This is because translations dilute the nuance of a statement. If a text has multiple topics,
sentiments, and themes, as reviews and comments usually do, the aggregate semantic score
of the data (+1 for positive, and -1 for negative) will be inaccurate with translations.
Applications of Korean NLP in Sentiment Analysis Tools
Repustate's Korean sentiment analysis API helps you get useful insights through
Social media listening
from Facebook, Twitter, Instagram, and even video-based platforms like TikTok and YouTube.
This is very useful to brands who want to capture sentiments around specific facets
of their business, product line, or service.
Analyze news - text, audio & video:
Sentiment analysis from news streams is really easy with Repustate's Sentiment Analysis API.
Whether you want Korean NLP analysis for employee surveys or product reviews, our tool gives
you relevant insights. Furthermore, its visualization dashboard converts the data into charts,
graphs, and tables, so you can understand the data easily.
Analyze surveys, forums, and Google reviews:
Companies dedicate a large number of resources to understanding their customers by
running feedback campaigns in their stores, social forums, mobile apps, and websites.
Repustate's Korean Voice of Customer analysis
helps you make sense of all that data.
Understand public sentiment:
From policy decisions to bad customer service, whatever the issue may
be, people share this information with the world. This data can be vital to
a government body, or a company looking to improve its brand perception and
Let Repustate's Korean sentiment analysis uncover your hidden insights
Real-World Applications of Korean Text Sentiment Analysis
Repustates's Korean sentiment analysis API is made for the Korean language and its dialects.
Powered by Korean NLP, the solution gives you accurate and fast insights through
aspect based sentiment analysis
of your data. Here are some real-world examples of how Repustate has provided Korean
sentiment analysis to organizations in various industries.
A Busan-based healthcare corporation wants to
understand the doctor-patient relationship to make the
whole system more sensitive towards healthcare, Repustate's
Korean sentiment analysis API can enable them to
semantically identify various aspects related to
doctor-patient interaction and sentiment related to those
aspects. With Repustate, this Busan-based
healthcare system can unlock the advanced applications of
NLP in Healthcare and have the answers they've been
searching for in half the time it would have taken before.
A Seoul-based financial corporation realizes that it's not good for business if they
are unable to monitor global financial and stock market news accurately and fast. Since even
names of companies change when the news is in a foreign language, the company realizes it is
missing out on vital information in an industry that thrives on quick decision-making.
Repustate's Korean sentiment analysis tool provides them with a highly-precise,
customized, stock sentiment analysis solution powered by Korean NLP.
Our sentiment analysis solution gives them insights into
market sentiment based on price movements of securities traded, as well as
financial news coverage in all major languages. They also leverage the
real-time dashboard that shows market sentiment scores and share prices for
different debt instruments and equities in an easy-to-understand format.
A public transport agency from Incheon wants to improve its service and brand perception
and approach Repustate with the problem. They want to provide better service by addressing
negative feedback and restructuring resources. They want to have an authentic and detailed
understanding of the sentiment of daily commuters using public modes of transport.
Repustate's Korean sentiment analysis API helps them understand their data. By applying
Korean natural language processing techniques to all the reviews and comments gathered
from public forums, Repustate's solution is able to give them useful insights they can use
to achieve their goals.
Korean Enterprise Search
Repustate's Enterprise Search automatically annotates
your Korean data with semantic information. This includes relevant entities, topics, and
entity-specific metadata. You can search all metadata associated with any given entity that
Repustate finds by market cap or industry type for business, or perform your search by nationality.
Over 100 metadata properties can be searched, and all of them can be automatically determined
by Repustate's Korean Enterprise Search.
언어라고 다 같은 게 아니다
문법 규칙은 언어마다 다 다릅니다. 동사 활용, 명사와 동사의 일치, 부정 표현에 관한 규칙이 언어마다 다 다르다는 이야기입니다.
한국어는 하나의 고유한 언어로, 영어와 여러 모로 다릅니다. 한국어 정서를 분석하면서 영어 정서를 분석할 때 쓰는 기술과 언어 모델을 똑같이 사용해 버리면 아주 부정확한 결과가 나오고 맙니다.
그래서 Repustate는 한국어 특화 정서 모델은 물론, 한국어 품사 식별기(tagger), 한국어 어휘 정리기(lemmatizer) 등 한국어 정서 분석에 일조하기 위한 한국어 특화 도구를 개발했습니다.
한국어 품사 식별
Repustate는 한국어 품사 식별을 이용해 문서의 한 단락 속에서 정서가 드러난 부분을 추립니다. 동사, 명사, 형용사는 정서를 알아내는 데 필요한 단서가 되기 때문입니다.
신속하고 정확한 한국어 품사 식별기를 만들기 위해서는 막대한 말뭉치가 필수인데, 이 말뭉치란 손수 품사를 식별해 놓은 한국어 문서를 가리킵니다. 이 한국어 문서는 기계 학습 알고리즘에 넣어 한국어 품사 식별기를 만드는 데 쓰입니다.
말뭉치가 클수록 좋은 것은 맞지만, 그보다 더 중요한 것은 다양해야 한다는 것입니다. 다양할수록 더 나은 한국어 품사 식별기를 만들 수 있습니다. Repustate는 넓은 범위를 보장하기 위해 다양한 출처에서 자료를 구해 막대한 양의 한국어 말뭉치를 만들어 냈습니다.
한국어 정서 모델
Repustate는 한국어를 글로 적을 때 정서가 드러나는 표현, 숙어, 구문을 다양하게 포착하고자 한국어에 특화된 정서 언어 모델을 개발했습니다. 또, 영어와 확연하게 구분되는 한국어의 고유하고 다양한 문법 특성을 이해한 덕에 한국어 정서를 매우 신속하고 정확하게 분석할 수 있습니다.