Sentiment analysis is the automated process of understanding an opinion about a given subject from written or spoken language. In a world where we generate 2.5 quintillion bytes of data every day, sentiment analysis has become a key tool for making sense of that data. This has allowed companies to get key insights and automate all kind of processes. But… How does it work? What are the different approaches? What are its caveats and limitations? How can you use sentiment analysis in your business? Below, you'll find the answers to these questions and everything you need to know about sentiment analysis. No matter if you are an experienced data scientist a coder, a marketer, a product analyst, or if you're just getting started, this comprehensive guide is for you. How Does Sentiment Analysis Work? Sentiment Analysis also known as Opinion Mining is a field within Natural Language Processing (NLP) that builds systems that try to identify and extract opinions within text. Currently, sentiment analysis is a topic of great interest and development since it has many practical applications. Since publicly and privately available information over Internet is constantly growing, a large number of texts expressing opinions are available in review sites, forums, blogs, and social media. With the help of sentiment analysis systems, this unstructured information could be automatically transformed into structured data of public opinions about products, services, brands, politics, or any topic that people can express opinions about. This data can be very useful for commercial applications like marketing analysis, public relations, product reviews, net promoter scoring, product feedback, and customer service. Before going into further details, let's first give a definition of opinion. Text information can be broadly categorized into two main types: facts and opinions. Facts are objective expressions about something. Opinions are usually subjective expressions that describe people's sentiments, appraisals, and feelings toward a subject or topic. In an opinion, the entity the text talks about can be an object, its components, its aspects, its attributes, or its features.
Humans are fairly sophisticated when it comes to understanding the complex meanings beneath the spoken or written word. For example, we can tell that a statement like, "My car had a flat. Brilliant!" is sarcastic, not actually brilliant. And with the help of machine learning, computers are beginning to get better at reading between the lines of our tweets, Facebook updates, and email messages, resulting in a new kind of analytics: sentiment analysis. Sentiment analysis, also known as opinion mining, seeks to determine the attitude of an individual or group regarding a particular topic or overall context – be it a judgment, evaluation, or emotional reaction – from text, video, or audio data.
A new app for Google Glass claims to be able to tell you exactly how someone is feeling. Called Emotient, is can tell whether a person is happy, sad angry or confused - and can monitor an entire room of people at once. The firm says the technology could even be used by advertisers to gauge reactions to their products. The Emotient software processes facial expressions and provides an aggregate emotional read-out, measuring overall sentiment (positive, negative or neutral); primary emotions (joy, surprise, sadness, fear, disgust, contempt and anger); and advanced emotions (frustration and confusion). The Emotient software detects and processes anonymous facial expressions of individuals and groups that the Glass wearer sees to determine an aggregate sentiment read-out; it does not store video or images.
"Man and machine always get a better answer than man alone or machine alone." "The robots are coming, the robots are coming!" said my colleague and artificial intelligence expert Kimberly Nevala in a tongue-in-cheek teaser for her new ebook, "Making Sense of AI." In fact, in the context of digital transformation and customer experience, artificial intelligence (AI) already has a foot in the door. And that foot is poised to kick the door wide open. IDC predicts that by 2019, 40 percent of digital transformation initiatives will be supported by some sort of cognitive computing or AI effort.
Salesforce launched three AI tools for developers today at the TrailheaDX developer conference. These algorithms, which fall under the new Einstein Platform Services, enable third-party developers to add Einstein intelligence to applications built on top of the Salesforce platform. The new services include sentiment and intent analysis and some pretty sophisticated image recognition analysis tools, that, when properly trained, can count objects and even recognize attributes like color or size. The three services open up all kinds of possibilities for developers to tap into these advanced technologies to build sophisticated functionality into apps built on top of Salesforce. The Einstein Intent tool allows programmers to understand the intent of customer inquiries, which could make it easier to automatically route leads, escalate service cases or personalize a marketing campaign through a custom app.