Repustate’s AI-driven Arabic sentiment analysis automatically identifies any positive or negative sentiment in your text.
Entities: مطعم, طعام, مشروب, قهوة عربية, السمك المشوي, كاليماري
And because it’s trained natively and doesn’t require translations, Repustate understands local sayings, idioms, and slang
|العرق يمد لسابع جد||الآباء والأبناء يشبهون بعضهم البعض|
|ضَرْبَة مِعَلِّم||عمل جيد|
|سَمْن عَلَى عَسَل||متوافقة تماما|
|اِسْتَعْرَض عَضَلاتُه||إظهار القوة|
|قد الدنيا||الافضل عالميا|
Repustate can even apply aspect-based sentiment analysis to your Arabic text and create a richer and more in-depth analysis of your data.
Repustate can find the Arabic text most relevant to you no matter where it is on the public internet . Repustate can even extract valuable semantic insights from Arabic videos on sites like YouTube and TikTok. Have your own Arabic data? No problem - simply upload your data to Repustate and let our Arabic text analytics pipeline do the rest.
Read more about Repustate’s Arabic sentiment mining solution
Table of Contents:
Repustate is the preferred Arabic sentiment analysis company because our sentiment analyzer 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 in the Middle East and the Arabic-speaking world collaborate with us.
Repustate has developed sentiment language models specific to Arabic to capture the various phrases, idioms, and expressions that help define certain sentiments. It turns unstructured Arabic data into business intelligence you can use to increase your value proposition, brand experience, and value delivery.
We collate a massive corpus of Arabic text, which is manually tagged and processed by an ML model for high precision in aggregate sentiment scoring. The steps can be defined as follow:
Collect a massive, highly varied corpus (collection of texts) of the manually tagged Arabic text.
Create an Arabic part-of-speech tagger.
Build lemmatization i.e. apply rules of conjugating nouns and verbs based on gender and tense.
Build prior polarity to determine the positive and negative context of a word.
Determine grammatical constructs to define negations and amplifiers.
Feed sentiment scores to train the model.
Repustate uses Arabic Named Entity Recognition (NER) to identify brand and business entities in data. No matter how misspelled a word is, our Arabic 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.
Translations yield incorrect results to a very high degree since they dilute the nuance of the word in the original language. Different languages have different grammatical constructs for negations, amplifiers, and root words. If a text has multiple topics and sentiments (as reviews and comments usually do) this difference in language rules renders the aggregate sentiment score of the data wrong. Therefore, translations can lead to incorrect insights that can be detrimental to a company’s return on investment and business.
As your Arabic sentiment analysis company, Repustate never translates your data. We use our own painstakingly collated, highly precise, individually developed Arabic part-of-speech tagger and lemmatizers. This is what ensures you the highest possible accuracy of sentiment scores from your data, so you can get the right insights.
Repustate's Arabic sentiment analysis API helps you get useful insights through Social media listening from Facebook, Twitter, Insta, 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.
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 Arabic Voice of Customer analysis helps you make sense of all that data.
Emotion analysis from news streams is really easy with an Arabic sentiment analysis company like Repustate. Whether you want an Arabic NLP solution 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.
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.