URDU SENTIMENT ANALYSIS

Unlock the meaning in your Urdu data.

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

Your Urdu 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 Urdu sentiment analysis?

Urdu 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 Urdu sentiment analysis would yield terribly inaccurate results.

Urdu Sentiment Analysis API

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

What are the basic steps in Urdu sentiment analysis?

Urdu 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 Urdu part-of-speech tagger, data scientists have to have a massive corpus of the manually tagged Urdu text. This text can then be fed into a machine-learning algorithm to create an Urdu part-of-speech tagger.

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

Taking it a step further, by using Urdu 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

Urdu language sentiment models

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

Applications of Sentiment Analysis Tools:

Analyze Twitter, Facebook, Instagram, and Youtube content:

People love to express their experience online in the form of product reviews, recommendations, and even tutorials. So be it through Facebook, Twitter, YouTube, or Instagram, now you can analyze sentiment in this massive flow of information to understand how your customers perceive you and your competitors. Unlock the power of social media with Repustate's Social media listening solution.

Analyse News - Text, Audio & Video:

Sentiment analysis from News streams is really easy with Repustate's Sentiment analysis API. We extract all the sentiment related to your business aspects from the live streams on a sentiment analysis dashboard. This helps the decision-makers understand market sentiment and take calculated risks.

Analyse Surveys, Forums, Website, and Google Reviews:

Companies dedicate a large number of resources to understand the voice of the customer by running feedback campaigns in their stores, social forums, mobile apps, and websites. They even get hundreds of reviews on Google, Yelp, and other review platforms. But they get overwhelmed by the sheer volume of feedback and complexity in generating a clear insight. This is where Repustate's Urdu Voice of Customer analysis kicks in, we go beyond just document level positive or negative feedback. We analyze each business aspect of all the reviews to help you dig deeper to generate actionable insights.

Understand public sentiment:

As it has become common practice for people to take to the internet and share their day-to-day experiences. From policy decisions to bad service at the government offices and slow-to-respond public websites, however mundane it might seem, people feel compelled to share this information with the world. This data can be priceless to a well-organized government, looking to improve public services.

Industrial applications of Text-based Sentiment Analysis in Urdu

Our Urdu semantic analysis API is optimized to understand and analyze your data in standard Urdu language and dialects. By combining sentiment analysis and named entity recognition, we can help companies identify essential business topics to conduct aspect based sentiment analysis for each topic. 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:

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 advaced 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:

To take a step further, let's take an example of a Karachi 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.

Government Sector:

Finally, let's take a look at how the Lahore based Government agency benefits from our text analysis tool. As a public transport agency, it would like to understand the sentiment of daily commuters using public modes of transportation. Repustate's Urdu sentiment analysis API 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.

Urdu Enterprise Search

Repustate's Enterprise Search automatically annotates your Urdu 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 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۔