Effective business decisions through sentiment analysis

Original article was published by Cedric Pal on Artificial Intelligence on Medium


Effective business decisions through sentiment analysis

Photo by Franki Chamaki on Unsplash

Sentiment analysis is a method of social listening or monitoring of conversations to identify and understand information. In simpler terms, it detects polarity — if texts, social media posts, reviews, or documents are positive, negative, or neutral, or if they cause reactions such as anger, happiness, as well as interest levels. It plays a significant role in marketing and helps organizations understand how people feel about their brand, product, or service. Brands can listen to their current and potential customers online and customize products and services to meet their specific needs. Technology and software have made it extremely simple and affordable for organizations to understand emotions as individuals openly express themselves on social media more than ever before. The ultimate purpose of sentiment analysis is to code emotional or evaluative content, with the same accuracy as humans, but faster.

Technology that drives it

It uses various Natural Language Processing (NLP) methods and machine learning algorithms, such as –

1) Rule-based systems: Analysis using a set of manually crafted rules (list of words or expressions) to help identify subjectivity, polarity, or the subject of opinion.

2) Automatic systems that rely on machine learning techniques (computer algorithms that improve with experience) to learn from data rather than manually created rules.

3) Hybrid systems that combine both rule-based and automatic approaches are often more effective than relying only on one system.

Caveats of sentiment analysis

Sentiment analysis does not guarantee 100% precision, even when undertaken by human beings, and usually reaches about 70–80% accuracy. It is often due to subjectivity and context, tone, variations by region or culture, misspellings, sarcasm and irony, emoticons, and comparisons. As with all applications of machine learning, software development companies are trying to find new techniques and innovation to improve accuracy levels.

Advantage for marketers

Sentiment analysis provides insights that can help drive efficient business decisions, strategies, and objectives. Some applications of sentiment analysis for marketers are –

1. Targeted marketing can identify individuals who are positive about your product or service but have not purchased it and can find patterns and likes to make specific offers.

2. Customer service application includes identifying issues, problems, and complaints before they escalate and become a crisis. Once identified, organizations can prepare solutions for common concerns and become proactive in their approach.

3. By tracking trends over time, it helps identify insights to plan campaigns, launches, and reorganization of processes.

4. It can monitor competition on social media and digital platforms, and by analyzing user response towards competitions’ products and services, organizations can identify and address gaps.

Use case

Apple is an excellent example of an organization implementing sentiment analysis through industry and competitor analysis. They use sentiment analysis to continually examine their brand value proposition, introduce new features, address issues, and analyze what matters most to the consumer to build products to fill those gaps. Apple identified customer pain points such as bad design, poor privacy, and user experience to deliver products that not only met the customers’ expectations but also raised the bar, forcing the competition to find new competitive advantages. They frequently analyze trends that revolutionize the industry. They were the first company to introduce fingerprint sensors, face ID, voice assistance in mobiles — Siri, and get rid of the headphone jack and chargers — to reduce e-waste. They are a successful company today because they make data-driven decisions.

Organizations need to understand the level to which sentiment analysis improves business. It is a decision that needs to be made at an executive level to integrate systems and processes and reap the full rewards of sentiment analysis.

Sources

Bilyk, Volodymyr. “5 Sentiment Analysis Real-Life Applications & Examples.” Theappsolutions.Com, theappsolutions.com/blog/development/sentiment-analysis-for-business/. Accessed 24 July 2020.

Gupta, Shashank. “Sentiment Analysis: Concept, Analysis and Applications.” Towards Data Science, Towards Data Science, 7 Jan. 2018, towardsdatascience.com/sentiment-analysis-concept-analysis-and-applications-6c94d6f58c17.

“Sentiment Analysis: Nearly Everything You Need to Know | MonkeyLearn.” MonkeyLearn, 20 June 2018, monkeylearn.com/sentiment-analysis/.

Walther, Charly. “Sentiment Analysis in Marketing: What Are You Waiting For?” CMSWire.Com, 2 Jan. 2019, www.cmswire.com/digital-marketing/sentiment-analysis-in-marketing-what-are-you-waiting-for/.