Acquiring Commonsense Knowledge for Sentiment Analysis through Human Computation

AAAI Conferences

Many Artificial Intelligence tasks need large amounts of commonsense knowledge. Because obtaining this knowledge through machine learning would require a huge amount of data, a better alternative is to elicit it from people through human computation. We consider the sentiment classification task, where knowledge about the contexts that impact word polarities is crucial, but hard to acquire from data. We describe a novel task design that allows us to crowdsource this knowledge through Amazon Mechanical Turk with high quality. We show that the commonsense knowledge acquired in this way dramatically improves the performance of established sentiment classification methods.

4 Ways Your Competitors Are Using AI to Improve their CX Marketing Insider Group


Forward-thinking companies are already looking back at the AI-driven strategies they have been using for years to find ways to get even more out of smart technology. The fact is, customer experience is the top priority for 71% of B2B businesses. Not that you need any more pressure, but you aren't only racing against your competitors to harness the power of AI for enhancing customer experience. What AI can do for personalization, customer engagement, and wowing your customers with a great experience today is remarkably more sophisticated than what companies were using AI to do a few years ago. If you aren't using AI intelligently to build better customer relationships, how can you compete with the companies who have been leveraging this technology for years?

Speech Analytics Will Change How We View AI Articles Analytics


Matt Matsui, SVP, Product Startegy and Marketing, Calabrio, notes that: 'Speech analytics is unique, especially compared to other types of analytics. When customers are speaking, they aren't necessarily filtering and forming conversations the way they would in an email, text message, or on social media. Speech analytics give companies access to in-the-moment reactions and sentiments because customers are using both contextual and functional words during conversations. Contextual words are the nouns and verbs used to formulate a sentence, while functional words are used to fill in the gaps. It's actually those functional or'throwaway' words that give insight into someone's subconscious, or his/her true feelings, and brands now have access to that insight.

Sentiment Analysis: nearly everything you need to know MonkeyLearn


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.