Natural Language Processing for Sentiment Analysis
Digital media represents a huge opportunity for businesses of any type to capture the opinions, needs and intent that users share on social media. In fact, the number of Google searches, WhatsApp messages and emails sent in 60 seconds is truly impressive (2,315,000 Google searches, 44,000,000 WhatsApp messages, more than 150,000,000 emails). Truly listening to a customer’s voice requires deeply understanding what they have expressed in natural language: Natural Language Processing (NLP) is the best way to understand the language used and uncover the sentiment behind it.
People often consider sentiment (in terms of positive or negative) as the most significant value of the opinions users express via social media. However, in reality emotions provide a richer set of information that address consumer choices and, in many cases, even determines their decisions. Because of this, Natural Language Processing for sentiment analysis focused on emotions is extremely useful. NLP for speech analysis, combined with a powerful social media monitoring strategy, organizations can understand customer reactions and act accordingly to improve customer experience, quickly resolve customer issues and change their market position.
However, without NLP and access to the right data, it is difficult to discover and collect insight necessary for driving business decisions. NLP makes speech analysis easier. For example, if a customer sends an email about a problem they’re experiencing with a product or service, a NLP system would recognize the emotion (angry, disappointed, annoyed) and mark it for a quick automatic response or forward the email to the right person. Similarly, a financial services company could use an NLP application for speech analysis to identify the sentiment in articles associated with specific stocks, or analyze reports to judge a stock’s performance and recommend whether to buy or sell the stock.
Natural Language Processing for sentiment analysis is being widely adopted by different types of organizations to extract insight from social data and acknowledge the impact of social media on brands and products.
Understanding the text in context to extract valuable business insight
Because human language is complex, understanding language is not easy for machines. Teaching a machine to analyze a text from the grammatical point of view, while considering cultural variations, slang and sarcasm that occur in blogs, forum comments or in email messages, etc., is a difficult process. Teaching a machine to understand how context can affect tone is even more difficult.
Without contextual understanding, a machine just looks for target words and automatically categorizes “amazing” as positive and “bad” as negative. Natural Language Processing speech analysis techniques are used to tag parts of speech, named entities and more, in order to help machines “read” text by simulating the human ability to understand language.
Insight for structured and unstructured data
Even though the analysis of unstructured data allows companies to manage, analyze and extract insight from billions of social media messages, tweets, blogs and conversations, companies must be able to integrate insights from NLP with structured data (such as surveys, tracking studies, focus groups, etc.) to get a more complete view of the picture. Matching metrics to business goals helps ensure that the next step you choose is one that can make a difference.