Sentiment Analysis for Arabic Tweets
Internet users in the Arab region have an average of 8.4 social media accounts, with about 75% of users saying that they’ve started using social media platforms such as Twitter and TikTok even more because of covid19 stay-at-home measures. A recent article in Arab News even emphasizes how popular Twitter is in Saudi Arabia alone. Given all this, there is just so much information out there in the form of comments and opinions that can be gleaned using sentiment analysis in Arabic tweets for impactful business intelligence - whether it is for voice of the customer (VoC) data, patient voice data, beauty, hospitality, or even in security and surveillance.
That’s why in this article I aim to give you a broad idea of how you can use Twitter sentiment analysis in Arabic to improve your social media listening skills for a more holistic and targeted business strategy in the Arab world. You will also see how Arabic sentiment analysis is empowering industries in MENA already.
What Are The Steps Of Sentiment Analysis In Arabic Tweets?
First things first, to develop and train a model for Arabic text analytics and sentiment analysis in Arabic tweets, we need to collate a massive corpus in Arabic. The more varied this corpus is, the better it will be when we train our AI-powered machine learning model. We then manually tag and categorize the corpus into entities. There are some Arabic training datasets that are readily available too. You can read about them here. Arabic NLP tasks are trained on this data, which is then processed for aggregate sentiment scoring.
Here are the steps that comprise Twitter sentiment analysis in Arabic.
Step 1: Collect a highly varied corpus of manually tagged Arabic text.
Step 2: Create an Arabic part-of-speech tagger so the algorithm can identify conjunctions, subordinate clauses, prepositions, and nouns.
Step 3: Apply rules of conjugating nouns and verbs based on gender and tense through lemmatization.
Step 4: Determine the positive and negative context of a word to build polarity.
Step 5: Determine grammatical constructs to define negations and amplifiers.
Step 6: Feed sentiment scores for the processed data to train the model.
By the third iteration, an Arabic sentiment model like Repustate’s can achieve an 85% accuracy standard. And the best part is that because it is built on artificial intelligence, the model keeps learning from each cycle of data processing and becomes smarter and smarter over time. Here is a comparison chart where you can see how Repustate’s sentiment analysis model gives the highest accuracy compared to its peers, thanks to its named entity recognition (NER) capability, and native language processing engine.
Click here to understand the sentiment analysis process in detail.
See Repustate's Arabic sentiment analysis in action.
Why Is A Native Model Essential For Analyzing Arabic Tweets?
Having a native Arabic language reading capability is extremely important in Arabic sentiment analysis. This is because Arabic is a complex language, with over 30 Arabic dialects. Most of them are derived from Modern Standard Arabic (MSA) or Classical Arabic, the standard version of Arabic. And because dialects are rooted in geo-regional dialogue patterns, they often possess different grammar rules, vocabularies, semantics, and syntax.
A text analytics API that reads Arabic and its dialects natively, overcomes the hurdles of these vernacular branches of Arabic. Only because of this feature does it give accurate results for sentiment analysis in Arabic tweets because it doesn’t lose any nuance, tense, gender, root word, or grammar rule due to translations.
How Does Sentiment Analysis In Arabic Tweets Help Businesses?
Businesses have a lot to gain through social media listening and sentiment analysis of Arabic tweets. They can analyze hybrid social platforms like Twitter or Facebook, where video content analysis can give a very clear picture of public sentiment through the analysis of both video content and data from comments. Three major areas through which emotion mining can help businesses are as follow.
- Content Analysis on Twitter, Facebook, Insta, TikTok, & YouTube
Brands can easily capture sentiments around specific aspects of their business, product line, or service through Twitter sentiment analysis in Arabic social media. This is especially useful in areas like hospitality, beauty & makeup, social causes & civic rights, or even industries like automotive and travel. Useful insights through emotion mining from Twitter, Facebook, TikTok, YouTube, and Instagram can catapult a company’s marketing efforts.
- Analysis of Surveys, Customer Forums, & Google Reviews
Feedback campaigns on social media platforms, online forums, mobile apps, and websites can all be leveraged through Arabic voice of the customer (VoC) analysis. A smart and scalable sentiment analysis solution can process millions of surveys, and analyze open-ended questions without downtime as it seamlessly finds patterns in data across review sites and customer forums.
- News Analysis - Website & Online Magazines, Podcasts & Videos
News streams are a vital part of online reputation management and sentiment analysis in Arabic tweets. A number of news websites have taken to Twitter to promote news content in video, image, and text form. Because of its short structure, Twitter has become a go-to source for a quick recap of events as well as for sharing opinions of world matters. Insights from sentiment analysis of news, documentaries, interviews, and such through podcasts, videos, and online magazines can be harnessed for improving business decisions on projects, marketing & advertising efforts, as well as investments.
- Understand Public Sentiment
Companies all over the world have to take into account public sentiment in today’s widespread cancel culture. In a way, this has increased social responsibility and coaxed businesses out of a relaxed attitude around social issues - from environmental and ethical sourcing standpoints to gender equality and diversity.
Aggregating sentiment to understand public opinion is vital to not only a private organization but also to a government body, or a business looking to improve its brand perception and market share. Twitter sentiment analysis in Arabic has helped many a business in keeping abreast of any potential negative sentiment and thwart crises in their public reputation.
Take a quick tour of Repustate's Arabic NLP solution
How Twitter Sentiment Analysis In Arabic Impacts Industries
Hospitality is a very competitive industry. One important advantage of using sentiment analysis for your restaurant, hotel, or spa is that you can monitor in real-time, how much ground you hold in popularity in comparison with your competitors as well as international hospitality industry benchmarks. Twitter sentiment analysis in Arabic can help you identify what exactly the social media buzz is about you and the industry in general, so you can make data-backed decisions for your growth strategy.
- Banking & Finance
Sentiment analysis in Arabic can be a boon for companies in the Arab world because they can be privy to patterns and trends in data gathered, both historical and current. These are extremely beneficial in extrapolating currency fluctuations due to regional issues or taking proactive measures to make banking services more efficient and customer-friendly.
Twitter has been a game-changer in which political parties promote themselves and their candidates. It is also at the forefront of helping people keep track of the views of political candidates, and measures that political parties take in achieving voter expectations. Twitter sentiment analysis to detect consistency between statements and actions promised by the government, and the public sentiment around different countries and their political stances, is crucial already in the MENA (Middle East & North Africa) region.
- Environmental Issues
Twitter is playing a crucial role in environmental issues and global warming concerns, thus helping green movements and promoting eco-friendly companies. Up until 2010, a sizable majority of people used to think that global warming was a hoax, but overall statistics suggest that the tides have turned and public opinion has changed noticeably. With sentiment analysis in Arabic tweets, comments, and videos, government bodies and private conglomerates are being held accountable for not following environmental preservation laws and being nudged for better policies to aid the green movement.
Sentiment analysis in Arabic tweets give a holistic perspective to patient voice data. Healthcare companies in the MENA region can access valuable feedback from employees, patients, and their caregivers, over a range of topics. This customer success story illustrates how Twitter sentiment analysis in Arabic and a powerful Arabic NLP-lead machine model can be used for granular data analytics in healthcare for patient voice and voice of the employee (VoE) data seamlessly from audio and video recordings as well as text.
Again, getting and understanding unbiased feedback from their target audience has helped the education vertical in the MENA region evolve. From smart mobile apps for education, to improved online academic infrastructure, as well as foreign students and teachers exchange programmes, the Arab world is taking stellar measures to enhance academic performance. All of this is being achieved through a growing interest in listening to the voice of the people both domestically and from expats. As an example, read this case study to see how welcoming and analysing feedback through Arabic Twitter analysis and from surveys has helped the Ministry of Egypt.
- Beauty & Makeup Industry
The beauty industry is heavily influenced by word-of-mouth advertising through social media. Whether it’s through user-generated content on Twitter, YouTube, TikTok, or Facebook, social media influencers have taken the business of the beauty industry to the next level just by, well, influencing people. Sentiment analysis in Arabic tweets and other social media platforms has helped companies like Huda Beauty, Sephora, and even a Japanese skin care company like Sakura Beauty to make a mark in the Arabic beauty and makeup market.
- Real Estate
Sentiment analysis in Arabic tweets, social media content, and customer review portals gives real estate governing bodies, policy-makers, and companies crucial information about public sentiment regarding issues like selling and asking prices, price fluctuations, and lending rates. Through automated description analysis, bigrams, and classifications of a sentiment analysis machine model, real estate companies are analysing trends and patterns in the industry. This has been instrumental in helping city councils and real estate companies develop strategies for recovery, promotion, and growth.
Reaping Business Growth With AI
All these amazing benefits of artificial intelligence are ready to be reaped through the simple integration of a text analytics engine. Most of these text analytics APIs are available as on-premise installations as well as on the cloud. Repustate’s own text analytics and sentiment analysis platform is available in both formats without downtime, and with seamless integration. But more important than everything else, it uses native Arabic natural language processing and named entity recognition (NER) to identify brand and business entities in data and analyze them. No matter how misspelled a word is, the Repustate Arabic NLP model will reproduce the name in the native script and give accurate name search, transliteration, and identity verification. This gives high-accuracy ranked results, based on the linguistic, phonetics, and specific cultural variation patterns of the names.
Reach out to us for more information on how sentiment analysis in Arabic tweets can help you with patient voice, VoC, or even VoE data in Arabic, like it has helped so many of our clients in MENA.
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