If you thought the ubiquitous dominance of artificial intelligence, ends with virtual personal assistants and facial recognition, you’ve got it wrong. There’s more to Artificial Intelligence than perceiving the type of coffee you prefer or chauffeuring high-end luxury cars; AI can now understand your emotions; whether your happy, sad, angry or even if you’re being sarcastic!
We get emotional, does AI?
The inventions in affective computing—blended with AI‑fueled advances in traditional text‑based sentiment analysis, brought forth the concept of being able to monitor emotions in conversations. These conversations can happen anywhere, even on chatbots, blogs, or social media; with the help of sentiment analysis, an AI can understand whether the convo is in favor of your brand or services. Imagine the capability to use an algorithm to determine what customers think about your product, why they conceive it in such a way and what can be done to make it better.
What Is Sentiment Analysis?
Sentiment analysis, also known as opinion mining is a tool that aims to explain emotions from a given text. Natural Language Processing (NLP) is used to extract and identify opinions from a given text.
In the most simplistic utilization, sentiment analysis will tell you if the customer response is positive or negative. The boon of sentiment analysis is that it is not restricted to just a small or large scale of data. For a larger scale, such as big data, AI can classify responses based on how people feel about a product. On a smaller scale, sentiment analysis can tailor a chatbot conversation to help the chatbot respond to a user’s reaction.
-
Advancing Chatbot Using Sentiment Analysis
While consistency is significant across communication platforms, you may find that customers tend to respond more genuinely to chatbots. Chatbots are intended to mimic the human reaction and streamline communication with users. These bots provide prompt acknowledgments, improving the customer experience, but solely following the inputted texts, without having their own responses.
The objective of an AI implemented chatbot, though, goes far beyond the substantive help provided to users: it simply understands their emotion and responds with that in their AI brain.
-
Sentiment Analysis in Social Media Monitoring
Social media is possibly the most significant quarry from which brands can mine their public opinion and accumulate data on their success or failure. Businesses require to pay attention to their social mentions on any platform, such as Facebook or Instagram where customers interact directly.
With the huge data on social media, it sure will be difficult to listen to every customer; therefore sentiment analysis within social media monitoring classifies the public opinions based on their emotions.
Hence, with the implementation of sentiment analysis in social media we can establish a starting point to understand the general public sentiment in aggregate. From which, public’s general emotions and responses are used to initiate campaigns or provide better services.
Conclusion
Meanwhile, Artificial intelligence is initiating innovative possibilities to rationalize investment in the core technology, where sentiment analysis will play a crucial role in the marketing domain. It will improve targeted sales messages and support industries in understanding consumer’s preferences. Sentiment analysis surely has a bright future ahead, one that will eventually find its way into AI voice platforms like Echo and Alexa, which may soon be able to understand a lot more than text inputs.
Author
Ricky : is a content strategist, who has an unparalleled dedication to building productive and engaging website content that attracts traffic and increase search engine rankings. If not typing furiously on his keyboard, Ricky is constantly learning ways to enhance search traffic acquisitions
