Our AI-driven Indonesian sentiment analysis API determines the sentiment in your Indonesian text. All analysis is done natively in Indonesian, leading to greater accuracy.
Repustate's sentiment analysis API is powered by Indonesian natural language processing (NLP). It analyzes Indonesian data natively, without the need to translate any document into English, thus, giving more accurate and meticulously useful insights.
Repustate's Indonesian sentiment analysis solution is made dedicatedly for the Indonesian-speaking world. The tool can be used to analyze data from any Indonesian source such as news, social media, blogs, reviews, and surveys.
The solution has its own dedicated Indonesian part-of-speech tagger, Indonesian lemmatizer, and Indonesian-specific sentiment models. It also understands colloquial words and industry jargon. Indonesian 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.
The Repustate Indonesian Sentiment Analysis solution is made specifically for Indonesian 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:
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.
Repustate has developed sentiment language models specific to Indonesian to capture the various phrases, idioms, and expressions that help define certain sentiments. It turns unstructured Indonesian data into business intelligence you can use to increase your value proposition, brand experience, and value delivery.
Repustate has a massive corpus of Indonesian text that has been tagged manually, as the first step towards Indonesian 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:
Repustate uses Indonesian Named Entity Recognition (NER) with advanced semantic search to identify brand and business entities in data. No matter how misspelled a word is, the Indonesian 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.
Trying to achieve Indonesian sentiment analysis by using an API that uses translations can yield incorrect results. So, the answer is, no. As your Indonesian sentiment analysis company, Repustate, uses intricate Indonesian 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.
Repustate's Indonesian 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.
Sentiment analysis from news streams is really easy with Repustate's Sentiment Analysis API. Whether you want Indonesian 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.
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 Indonesian Voice of Customer analysis helps you make sense of all that data.
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 market share.
Repustates's Indonesian sentiment analysis API is made for the Indonesian language and its dialects. Powered by Indonesian 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 Indonesian sentiment analysis to organizations in various industries.
When the prestigious Nahdi Medical Company, based in Saudi Arabia, approached Repustate, its biggest motivation was the need to understand patients better. They wanted to overhaul the system and deliver a better patient experience. They remained dissatisfied with the speed and accuracy of other tools in the market as they could not grasp the complexity of Indonesian. Repustate's sentiment analysis API was at last able to help the client meet its objectives. Our Indonesian NLP-powered solution gathered impactful insights more accurately and faster than others. It semantically identified various aspects related to patient care and provided the client with key revelations.
A Jakarta-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 Indonesian sentiment analysis tool provides them with a highly-precise, customized, stock sentiment analysis solution powered by Indonesian 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 Surabaya 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 Indonesian sentiment analysis API helps them understand their data. By applying Indonesian 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.
Repustate's Enterprise Search automatically annotates your Indonesian 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 Indonesian Enterprise Search.
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 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.
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.