Unlock the meaning in your Indonesian data.

Our AI-driven Indonesian sentiment API determines the sentiment in your Indonesian text. All analysis is done natively in Indonesian, no translations are used leading to greater accuracy.

Your Indonesian Sentiment Analysis Company

The Challenge

Not all languages are the same; grammar rules vary from one language to another and the rules of verb conjugation, noun-verb agreement, and negations vary from one language to another. This can make it extremely difficult to gauge text analytics without the proper tools in place to understand the nuances of other languages.

Should translations be used for Indonesian sentiment analysis?

Indonesian is a unique language and it differs from English in a number of ways: from sentence structure to words and phrases that may be used differently, using the same techniques and language models that work for English sentiment analysis when conducting Indonesian sentiment analysis would yield terribly inaccurate results.

Repustate Sentiment Analysis

Because of the challenges for applying high-level sentiment analysis for Indonesian companies, Repustate has developed Indonesian-specific tools to decipher words, industry jargon, and the feelings behind your customer's words. Indonesian sentiment analysis, including an Indonesian part of speech tagger, an Indonesian lemmatizer, and of course, Indonesian-specific sentiment models.

What are the basic steps in Indonesian sentiment analysis?

Indonesian part-of-speech tagging allows us to narrow in on where the sentiments may lie within a block of text. Verbs, nouns, and adjectives provide the cues necessary to determine sentiment and aid in detailed analysis. In order to create a fast and accurate Indonesian part-of-speech tagger, data scientists have to have a massive corpus of manually tagged Indonesian text. This Indonesian text can then be fed into a machine-learning algorithm to create an Indonesian part-of-speech tagger.

The larger the corpus, and more importantly, the more varied the corpus, the better the results in creating the Indonesian part-of-speech tagger. Repustate has created a massive corpus of Indonesian text grabbing data from a variety of sources to ensure good coverage.

Taking it a step further, by using Indonesian Named Entity Recognition with the advanced semantic search for enterprises, we can identify brand and business entities in data. No matter how misspelled a word is, our API will reproduce the first name in native script and thus improve the accuracy of name searching, transliteration, and the grade of identity verification initiatives. This gives high accuracy ranked results, on the basis of the linguistic, phonetic, and specific cultural variation patterns of the names

Indonesian language sentiment models

Repustate has developed sentiment language models specific to Indonesian to capture the various phrases, idioms, and expressions that help define sentiment when writing in Indonesian. Understanding the various grammatical aspects of the Indonesian language that make it unique and very different from English is what allows Repustate's Indonesian sentiment analysis to be as fast and as accurate as it is.

Applications of Text-based Sentiment Analysis in Indonesian

Our Indonesian semantic analysis API is optimized to understand and analyze your data in standard Indonesian language and dialects. Our API can be used to analyze the public sentiment about a product, service, government policy opinions, stock market trends, and social media listening. Let`s take a look at a few applications:

A Java based healthcare corporation wants to understand the doctor-patient relationship to make the whole system more sensitive towards healthcare, Repustate’s Indonesian sentiment analysis can enable them to semantically identify various aspects related to doctor-patient interaction and sentiment related to those aspects. With Repustate’s sentiment and text analytics, this Java based healthcare system can have the answers they’ve been searching for in half the time it would have taken before.

To take a step further, let's take an example of a Jakarta based finance corporation that deals in the forex market and monitors on International market news to tap into market trends. Repustate’s forex sentiment analysis unleashes a whole new arena for growth opportunities for the corporation by projecting and alerting them with semantically driven trends in the market.

Finally, let’s take a look at how the Surabaya based Government agency benefits from sentiment analysis. As a public transport agency, it would like to understand the sentiment of daily commuters using public modes of transportation. Repustate’s sentiment analysis can find the hidden sentiment through social media channels and social forums and understand user opinion about the service to decide what they need to update in their system to have happy customers.

Indonesian Enterprise Search

Repustate's Enterprise Search automatically annotates your Indonesian data with semantic information. This includes relevant entities, topics, and entity-specific metadata. Search any and all metadata associated with any given entity that Repustate finds. You can search 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 Indonesian Enterprise Search.

Tak semua bahasa sama

Aturan tata bahasa berbeda-beda antar bahasa. Aturan konjugasi kata kerja, kesesuaian kata benda-kata kerja dan negasi bervariasi antar bahasa.

Bahasa Indonesia adalah bahasa yang unik dan berbeda dari Bahasa Inggris dalam beberapa hal. Menggunakan teknik dan model bahasa yang sama dengan yang digunakan untuk analisis sentimen bahasa Inggris ketika melakukan analisis sentimen Bahasa Indonesia akan memberikan hasil yang sangat tidak akurat.

Itu sebabnya Repustate mengembangkan alat-alat yang spesifik untuk Bahasa Indonesia untuk membantu dalam analisis sentimen Bahasa Indonesia, termasuk penanda bagian wicara Bahasa Indonesia, pembuat lema Bahasa Indonesia, dan tentu saja model sentimen Bahasa Indonesia.

Penandaan bagian wicara Bahasa Indonesia

Penandaan bagian wicara Bahasa Indonesia memungkinkan Repustate untuk mempersempit ke mana kemungkinan adanya sentimen dalam suatu blok teks. Kata kerja, kata benda, dan kata sifat, memberikan petunjuk yang diperlukan untuk menentukan sentimen.

Untuk membuat penanda bagian wicara Bahasa Indonesia yang cepat dan akurat, Anda harus memiliki korpus besar teks Bahasa Indonesia yang ditandai secara manual. Teks Bahasa Indonesia ini selanjutnya dapat dimasukkan ke algoritma pembelajaran mesin untuk membuat penanda bagian wicara Bahasa Indonesia.

Semakin besar korpus, dan lebih penting lagi, semakin banyak variasi korpus, semakin baik hasil dalam membuat penanda bagian wicara Bahasa Indonesia. Repustate telah membuat korpus teks Bahasa Indonesia yang amat masif, mengumpulkan data dari berbagai sumber untuk memastikan cakupan yang baik.

Model sentimen Bahasa Indonesia

Repustate telah mengembangkan model sentimen bahasa yang spesifik untuk Bahasa Indonesia untuk menangkap berbagai frasa, idiom, dan ungkapan yang membantu menentukan sentimen saat menulis dalam Bahasa Indonesia. Memahami berbagai aspek tata bahasa dalam Bahasa Indonesia yang membuatnya unik dan amat berbeda dari Bahasa Inggris itulah yang memungkinkan analisis sentimen Bahasa Indonesia dari Repustate bisa menjadi cepat dan akurat seperti saat ini.