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Fast, accurate Urdu sentiment analysis. No translations needed.

Repustate draws insights directly from any native Urdu text to give you in-depth, actionable data analysis with one click.

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Your Urdu Sentiment Analysis Company

Repustate's sentiment analysis API is powered by Urdu natural language processing (NLP). It analyzes Urdu data natively, without the need to translate any document into English, thus, giving more accurate and meticulously useful insights.

Table of Contents:

  1. Urdu Sentiment Analysis API
  2. Benefits of Repustate's Urdu Sentiment Analytics
  3. Why Choose Repustate Over Others?
  4. Basic Steps in Urdu Sentiment Analysis
  5. Applications of Sentiment Analysis Tools
  6. Real-World Urdu Sentiment Analysis Examples
  7. Urdu Enterprise Search

Urdu Sentiment Analysis API

Repustate's Urdu sentiment analysis solution is made dedicatedly for the Urdu-speaking world. The tool can be used to analyze data from any Urdu source such as news, social media, blogs, reviews, and surveys.

The solution has its own dedicated Urdu part-of-speech tagger, Urdu lemmatizer, and Urdu-specific sentiment models. It also understands colloquial words and industry jargon. Urdu 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 Urdu sentiment analysis in action

What are the benefits of Repustate's Urdu Sentiment Analytics Tool?

The Repustate Urdu Sentiment Analysis solution is made specifically for Urdu 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:

  • Understand your Urdu speaking customers
  • Visualize all the insights in a sentiment analysis dashboard
  • Obtain granular Urdu 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.

  1. Premium Support: As our client, you will have a dedicated engineer assigned to your account.
  2. Technology: We apply artificial intelligence and machine learning to Urdu natural language processing for the best results.
  3. Urdu Text: We never translate. Repustate has unique speech taggers and sentiment models made just for Urdu text analysis. In fact, each of our 23 languages has an ML model based on the natural language natively.
  4. Accuracy: Our aspect-driven approach to sentiment is granular, accurate, and targeted.
  5. Speed: Our API can process 1,000 comments per second.
  6. Customizable: We custom-build the model to capture your most important aspects and topics.
  7. Scale: Our API can easily scale from 1 to 10 million to 100 million documents and beyond.
  8. Integration: The Repustate solution integrates seamlessly with your existing technology. You do not need 3rd party support for any underlying technology.
  9. Competition: Our model is the most accurate in NER compared to the competition that includes Google, Amazon, and Microsoft. Check out our comparison.
  10. Deployment Flexibility: We offer both, a Cloud model, and an on-premise installation for data security, in a one-click solution.
  11. No Hidden Fees: Our pricing includes support, updates, training, and deployment.
  12. Iterative: Our models get more and more intelligent with training and as they process more data.

Urdu Language Sentiment Models

Repustate has developed sentiment language models specific to Urdu to capture the various phrases, idioms, and expressions that help define certain sentiments. It turns unstructured Urdu data into business intelligence you can use to increase your value proposition, brand experience, and value delivery.

Take a quick tour of Repustate's Urdu text analytics solution

What are the Basic Steps in Urdu Sentiment Analysis?

Repustate has a massive corpus of Urdu text that has been tagged manually, as the first step towards Urdu 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:

  1. Collect a massive, highly varied corpus (collection of texts) of the manually tagged Urdu text.
  2. Feed this text into an ML-based algorithm to create an Urdu part-of-speech tagger.
  3. Granulate the algorithm for deeper accuracy using NER.
  4. Extract the sentiment score for each aspect, theme, and topic.

Repustate uses Urdu Named Entity Recognition (NER) with advanced semantic search to identify brand and business entities in data. No matter how misspelled a word is, the Urdu 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 Urdu sentiment analysis?

Trying to achieve Urdu sentiment analysis by using an API that uses translations can yield incorrect results. So, the answer is, no. As your Urdu sentiment analysis company, Repustate, uses intricate Urdu 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 Urdu NLP in Sentiment Analysis Tools

Analyze Twitter, Facebook, Insta, TikTok, & YouTube content:

Repustate's Urdu 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 Urdu 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 Urdu 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 market share.

Let Repustate's Urdu sentiment analysis uncover your hidden insights

Real-World Applications of Urdu Text Sentiment Analysis

Repustates's Urdu sentiment analysis API is made for the Urdu language and its dialects. Powered by Urdu 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 Urdu sentiment analysis to organizations in various industries.

Healthcare Sector:

A Islamabad-based healthcare corporation wants to understand the doctor-patient relationship to make the whole system more sensitive towards healthcare, Repustate's Urdu 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 Islamabad-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.

Financial Sector:

A Karachi-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 Urdu sentiment analysis tool provides them with a highly-precise, customized, stock sentiment analysis solution powered by Urdu 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.

Government Sector:

A public transport agency from Lahore 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 Urdu sentiment analysis API helps them understand their data. By applying Urdu 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 Urdu 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 Urdu Enterprise Search.

تمام زبانیں ایک جیسی نہیں ہوتیں

ہر زبان میں صَرف و نحو کے قوانین مختلف ہوتے ہیں۔ فعل کی گردان، اسم اور فعل کی باہمی ہم آہنگی اور نفی و ابطال ہر زبان میں مختلف ہوتے ہیں۔

اردو ایک منفرد زبان ہے اور یہ کئی لحاظ سے انگریزی سے مختلف ہے۔ انگریزی کے احساسات و جذبات کے تجزیے کے لیے استعمال ہونے والی تیکنیکس اور ساخت کو اگر اردو زبان میں احساسات و جذبات کے تجزیے کے لیے ہو بہو استعمال کرلیا جائے تو بھیانک حد تک غلط نتائج سامنے آسکتے ہیں۔

یہی وجہ ہے ہے ریپوسٹیٹ ( Repustate ) نے اردو زبان میں احساسات و جذبات کے تجزیے کے لیے اردو کے لیے مخصوص ٹولز تخلیق کیے ہیں، جس میں اردو کے اجزائے کلام کا شناخت کنندہ، اردو کے الفاظ کا مصدر بنانے والا، اور ظاہر ہے کہ اردو کے لیے مخصوص جذبات و احساسات کے نمونے شامل ہیں۔

اردو کے اجزائے کلام کی شناخت

اردو کے اجزائے کلام کی شناخت کی مدد سے ریپوسٹیٹ ( Repustate ) متن کے مخصوص حصے میں موجود احساس و جذبات کو مرتکز کرسکتا ہے۔ افعال، اسماء اور صفات، احساس و جذبات کے تعین کے لیے اشارے مہیا کرتے ہیں۔

اردو کے اجزائے کلام کا ایک تیز رفتار اور درست شناخت کنندہ تخلیق کرنے کے لیے، آپ کے پاس دستی طور پر شناخت کردہ اردو متن کا ایک ضخیم ذخیرہ ہونا چاہیے۔ اس کے بعد اردو کے اجزائے کلام کا شناخت کنندہ تخلیق کرنے کے لیے اس اردو متن کو بذریعہ مشین آموزکار حسابی عمل (الگورتھم) میں داخل کیا جاسکتا ہے۔

یہ ذخیرہ جتنا بڑا ہوگا، اور خاص طور پر، جتنا متنوع ہوگا، اردو کے اجزائے کلام کا شناخت کنندہ تخلیق کرنے کے نتائج بھی اتنے ہی بہتر ہوں گے۔ ریپوسٹیٹ ( Repustate ) نے ہر پہلو کا مکمل احاطہ کرنے کے لیے کئی ذرائع سے مواد حاصل کرتے ہوئے اردو متن کا انتہائی عظیم الشان ذخیرہ تخلیق کیا ہے۔

اردو زبان کے احساسات و جذبات کے نمونے

ریپوسٹیٹ ( Repustate ) نے مختلف فقروں، محاوروں اور بیانیہ اظہارات کا احاطہ کرنے کے لیے اردو میں استعمال ہونے والے مخصوص احساسات و جذبات پر مبنی زبان دانی کے نمونے تخلیق کیے ہیں جو اردو تحریر کرتے ہوئے متعلقہ احساسات و جذبات کی توضیح کرتے ہیں۔ اردو زبان کو انگریزی سے منفرد اور ممتاز کرنے والے صَرف و نحو کے مختلف پہلوؤں کو سمجھنا ہی ریپوسٹیٹ ( Repustate ) کے اردو احساسات و جذبات کے تجزیے کو اس قدر تیز رفتار اور درست بناتا ہے۔