This is Part One of the Marketing Technology primer on “Sentiment Analysis” and features the top definitions from the industry
A lot has changed in the way brands market their products to their customers. However, the biggest challenge continues to haunt every marketer even in the New Year — Do I know the exact emotion or sentiment of my buyers’ group? To answer that, marketing teams are hugely focused at relying on the current crop of Marketing Technologies for Sentiment Analysis.
A Quick Overview of Sentiment Analysis
Emotions drive the current trends in shopping and buying any product. Be it from a store or online, there are at least 150 or more emotional tones driving the choice any customer makes before buying any product. It’s a love-hate relationship between the product/brand and the customer.
Sentiment Analysis: What the Industry Says…
We dug through hundreds of reports and blogs on Sentiment Analysis before zeroing on to the top sources that clearly defined — “What is Sentiment Analysis with Marketing Technology?”
In a chat recently, Certain’s CMO Kristen Alexander said, “We spend time with our customers and in the industry educating and advocating for not only a personalized approach, but also an approach that enables face-to-face interactions as much as possible. In our personal lives, face-to-face interactions are critical to building and maintaining successful relationships. This is true in business as well — the trick is capturing these interactions from a data perspective. I help marketing peers think through what kind of data they can capture and how they can use that data to execute a highly successful enterprise marketing approach.”
Definition 1
According to Lexalytics –
“Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral.”
The definition is further broken down to suggest that Sentiment Analysis combines the use of Natural Language Processing (NLP) and various Machine Learning techniques to measure the impact of emotion on shopping behavior. It is based on elaborate marketing research, leveraging the expertise of Data Analytics companies to connect Sentiment Analysis with these technologies –
- Customer Experience Management
- Social Media Monitoring
- Workforce Analytics
- Artificial Intelligence and Predictive Analytics
Definition 2
According to MonkeyLearn –
“Sentiment Analysis is also known as Opinion Mining, built within NLP to identify, extract and analyze opinions within texts and expressions.”
The detailed description on Sentiment Analysis reveals the role of NLP in classifying opinions from an individual, organization or group of buyers into “Subjective or Objective,” or “Negative, Positive, and Neutral.”
The scope of sentiment analysis using Marketing Technology could be at these three levels of text classification –
- Document or Paragraph
- Sentence
- Sub-sentence/ phrase
Definition 3
According to the European Masters Program in Language and Communication Technologies (LCT), an Erasmus+ Program of the European Union.
“Sentiment Analysis is done using NLP, statistics, or Machine Learning methods to extract, identify, or otherwise characterize the sentiment content of a text unit.”
Types of Sentiment Analysis Approaches Using AI and Machine Learning
In 2019, we could identify that Sentiment Analysis approaches are broadly classified into three categories, based on the level of human-machine interaction and the extent to which distinct emotions can be mined by Machine Learning algorithms.
These approaches are:
- Rule-based approach leveraging Text classification, and Tokenization
- Automatic response based on Neural Networks
- Hybrid Sentiment Analysis algorithms utilizing the above two approaches
According to Noah Jacobson, TapClicks’ VP of Corporate Development, 2019 will be the year where “Customer Voice Will Grow in Value.” Noah said, “As brands continue tailoring shopping experiences for customers, they will increase customer interaction and solicitation of feedback. Consumer engagement with the brand will be one of the vehicles used to create more customized products and position the brand as part of a customer’s everyday life.”
Top Marketing Technology Innovators in Sentiment Analysis
- Google via Google Alerts and Google Map
- Amazon’s AWS Comprehend
- Nuance Analytics for Speech Recognition
- Meltwater
- OpenText
- Hootsuite Insights (formerly uberVu via Hootsuite)
- Talkwalker
- Brandwatch
Putting an emphasis on the role of AI for Sentiment Analysis, Aki Technologies CEO Scott Swanson said, “AI will continue to be a major theme in marketing but in order for it to deliver against the hype, it needs to be embraced as a solution to existing problems. Right now it’s really tempting to focus on how AI will transform marketing into something we’ve yet to imagine, but the most immediate way for marketers to extract value is to put it to work for the usual objectives, like impact and efficiency. That means smarter targeting, more relevant messaging and far less friction in the ad experience. In 2019, the goal shouldn’t be to get consumers excited about AI, but rather to create AI-powered marketing that gets consumers more excited about brand and products.”
As we sink into the New Year, we believe that social media and Big Data from IoT and workplace would be the top sources of mining opinions driving the Sentiment Analysis engine for Marketing, Sales, Customer Service and who knows — maybe the HR Technology industry as well.