How to Leverage AI for Sentiment Analysis on Social Media, CIO News, AND CIO

In a world where a single tweet can make or break a brand, it’s crucial for businesses and brands to invest in social media automation and analytics to gain actionable insights into brand perception. . You wouldn’t like to wait 12 hours to respond to that negative comment when #quit is prefixed by your brand trends on Twitter and Instagram, would you?

Studies have shown that customers tend to be more vocal and outspoken with their opinions on social media. The way they perceive a particular brand, its products/services fundamentally influences their behavior. So for brands, being able to deepen comments, replies, conversations, etc. customers can help uncover an unbiased view of their customers’ behavior and personality, helping them better understand customers’ intentions and feelings.

Contextual Sentiment Analysis

Social media data comes with lots of emojis, GIFs, and stickers that need to be translated into text for the AI ​​models to understand and react to. And this is commonly referred to as sentiment analysis. Traditional sentiment analysis is a classification of text to analyze whether the user’s statement/behaviour is positive, negative or neutral. But today, brands want to go beyond that and do contextual sentiment analysis to understand how users perceive a particular brand and products at any given time.

For example, Fenesta, a door and window manufacturing company. Fenesta uses AI-powered moderation tools, which essentially means that all content posted on its pages is reviewed and filtered automatically, faster and at scale. “Inappropriate content can be flagged and blocked from being published almost instantly. These can include product and service reviews, sentiment, pictures and images uploaded to our pages, spam or fake posts, etc. .,” said Sushmita Nag, Marketing Manager at Fenesta.

AI in the works

While a comment receives a response in real time via a machine, what happens in the background?

“Traditional non-AI/ML/NLP-based approaches have been shown to be insufficient for sentiment analysis tasks. Brands are finally waking up to the power of AI/ML & NLP to perform accurate sentiment analysis in consumer comments and feedback. Real-world consumer feedback and feedback requires deep learning techniques to properly classify sentiment, understand intent, and extract entities,” explained Gaurav Kachhawa, Chief Product Officer, Gupshup.

For example, sarcasm, laconicism, context-dependent language, multilingual comments, etc. are some of the challenges that can only be addressed by deep learning models. Additionally, these models often need to be refined with domain-specific datasets.

Developing a good enough or excellent Natural Language Processing (NLP) engine is vitally important to deriving value from social media engagement. But Manoj Malhotra, CTO, co-founder, Amplify.ai, thinks NLP suffers from a classic “chicken and egg” problem.

“If your engine isn’t above the threshold, you won’t get a large enough conversational data set to apply machine learning to keep improving it,” Malhotra said.

Sentiment analysis is part of machine learning and NLP that helps conversational AI classify user emotions in text and voice data into positive, negative or neutral emotions and other complex emotions identified by the user input. Most sentiment analysis solutions use Natural Language Understanding (NLU)-based intent recognition to understand the user’s intent in every query, regardless of sentence complexity. The NLU is also able to identify multiple intents and give instant resolution to the query/argument.

Mohamed (Mo) Zahid, VP, Demand Generation, Hootsuite believes that sentiment analysis, like all machine learning, is a “brute force” technique for analyzing millions, if not billions, of lines of text. The advantage here is the speed and scale at which it can mark passages of text. This makes it perfect for analyzing social media data.

He further explained that sentiment analysis does two things, it counts words and scores them.

“So, for example, a sentiment analysis model will read a passage, and it will identify a word as ‘terrible’, it will apply a negative classification to that word (it could be a binary like yes/no or a score like – 5 ) and will run through the whole passage (which could be hundreds, thousands or even millions of words) At the end it can have an output like +50 positive words and -100 negative words and do the evaluation that this is overall a ‘negative’ feeling passage (50-100=-50). It might also tell you things like the word terrible is present 34 times,” he said. Explain.