Word of the Day: sentiment analysis (opinion mining)

Word of the Day Daily updates on the latest technology terms | June 4, 2019 sentiment analysis (opinion mining) Sentiment analysis, also referred to as opinion mining, is an approach to natural language processing (NLP) that identifies the emotional tone behind a body of text. This is a popular way for organizations to determine and categorize opinions about a product, service or idea. It involves the use of data mining, machine learning (ML) and artificial intelligence (AI) to mine text for sentiment and subjective information. Sentiment analysis systems help organizations gather insights from unorganized and unstructured text that comes from online sources such as emails, blog posts, support tickets, web chats, social media channels, forums and comments. Algorithms replace manual data processing by implementing rule-based, automatic or hybrid methods. Rule-based systems perform sentiment analysis based on predefined, lexicon-based rules while automatic systems learn from data with machine learning techniques. A hybrid sentiment analysis combines both approaches. In addition to identifying sentiment, opinion mining can extract the polarity (or the amount of positivity and negativity), subject and opinion holder within the text. Furthermore, sentiment analysis can be applied to varying scopes such as document, paragraph, sentence and sub-sentence levels. Vendors that offer sentiment analysis platforms or SaaS products include Brandwatch, Hootsuite, Lexalytics, NetBase, Sprout Social, Sysomos and Zoho. Businesses that use these tools can review customer feedback more regularly and proactively respond to changes of opinion within the market. Types of sentiment analysis Fine-grained sentiment analysis provides a more precise level of polarity by breaking it down into further categories, usually very positive to very negative. This can be considered the opinion equivalent of ratings on a 5-star scale. Emotion detection identifies specific emotions rather than positivity and negativity. Examples could include happiness, frustration, shock, anger and sadness. Intent-based analysis recognizes actions behind a text in addition to opinion. For example, an online comment expressing frustration about changing a battery could prompt customer service to reach out to resolve that specific issue. Aspect-based analysis gathers the specific component being positively or negatively mentioned. For example, a customer might leave a review on a product saying the battery life was too short. Then, the system will return that the negative sentiment is not about the product as a whole, but about the battery life.   Continue reading... Quote of the Day "In human-machine hybrid decision systems -- such as customer support -- immediate sentiment analysis is the ideal solution." - Scott Robinson Trending Terms natural language processing data mining text mining machine learning unstructured text demographic Learning Center 6 tips for effective customer data mining Effective customer data mining can lead to future sales and enhanced customer service through shopping recommendations and fewer opportunities to mishandle data. Get six tips for mining customer data. AI at the edge spurs decentralization, IoT interconnectivity Distributed artificial intelligence is changing the way AI is used in real-world applications --spurred, in part, by the rapid decision-making and locality needed to sustain IoT. AI at the edge is here and hopes to unify and evolve IT in the enterprise. Beyond customer sentiment: How to put NLP technology to work Natural language processing is everywhere, thanks to Alexa, Siri and Google Home. While NLP technology is commonly used to measure customer sentiment, it can also help enhance training programs, improve job interviews and enable efficiency, explains expert Scott Robinson. New HR tools probe employee sentiment, feelings Sentiment analysis is a tool used to discover employee sentiment, such as feelings expressed in written responses to survey questions or in emails or chats. It's based on emerging new technologies that can identify emotions. HR is beginning to use these tools. Get started with natural language processing APIs in cloud AI technologies have become more common in cloud app development, and AWS, Azure and Google have taken notice. They now offer various services, such as natural language processing APIs, to help enterprises bulk up app capabilities. Compare these services, and dig into the basics to get started. Quiz Yourself Bad customer service news spreads like wildfire on Twitter. The wise company deals with customer complaints in an __________ manner. a. empathic b. empathetic Answer Stay in Touch For feedback about any of our definitions or to suggest a new definition, please contact me at: mrouse@techtarget.com Visit the Word of the Day Archives and catch up on what you've missed! FOLLOW US About This E-Newsletter This e-newsletter is published by the TechTarget network. To unsubscribe from Whatis.com, click here. Please note, this will not affect any other subscriptions you have signed up for. TechTarget, Whatis, 275 Grove Street, Newton, MA 02466. Contact: webmaster@techtarget.com Copyright 2018 TechTarget. All rights reserved.