Arabic Sentiment Analysis

Arabic Sentiment Analysis Real-World Use Cases

Overview Take a look at the top 3 real-world examples of how AI-based Arabic natural language processing (NLP) empowers businesses. Machine translations can have adverse effects when used for deriving sentiments owing to the intricacies of the Arabic language such as inflectional writing, left-to-right directions, semantic ambiguity, lack of traditional vowels, and other such issues. The examples showcased in this article prove how data gathered from varied sources can be richly harnessed for consumer insights when an Arabic sentiment analysis model is powered by native Arabic NLP.

Importance of Arabic NLP in business Arabic is a very complex language. Having a sentiment analysis solution that relies on an intelligent Arabic NLP model rather than the traditional system of using translations, is of critical importance. Unlike most languages, Arabic has three major linguistic issues - syntactic ambiguity, diacritical marks that denote vowels that are not present in online texts, and a lack of punctuation that makes long paragraphs difficult to decipher by machine learning algorithms. When an Arabic sentiment analysis model translates words to another language, say English, it loses the nuance of the text, thus leading to incorrect aggregate sentiment analysis. This can translate to businesses adopting wrong strategies based on incorrect insights, which in turn can be detrimental to a company’s return on investment and overall business planning.

How we do Arabic sentiment analysis at Repustate

A massive corpus of varied Arabic data is collected to train the Arabic sentiment analysis model. A part of the resulting data is tested and then compared to an existing dataset. The Arabic NLP model is trained again until it gives the highest accuracy scores. The steps are as follow: Step 1: Part-of-speech tagger Each Arabic word is classified at a grammatical level to identify conjunctions, subordinate clauses, prepositional, and noun phrases. This helps the model understand the text’s true meaning.

Step 2: Lemmatization In this step, the rules of conjugating nouns and verbs based on gender, tense, etc. are applied in the model. This helps the tool determine the root of a word.

For example, “reading” and “reader”, are based on the root word “read”.

Step 3: Prior polarity We determine the positive and negative context of a word and calculate the intensity of the polarity. For example, excellent (+1), good (+0.5), average(0), and poor(-0.5). This is what helps the Arabic sentiment analysis solution to provide scores.

Step 4: Grammatical constructs We determine nuanced grammatical constructs like negations and amplifiers so the model can understand sentiment scores.

Step 5: Sentiment scores When all these steps using Arabic NLP come together, the sentiment scores are fed into machine learning models. Now the model can assign scores related to an aspect or entity when it reads a text.

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