Brand Monitoring with Sentiment Analysis – Fundamentals of Natural Language Processing

Brand Monitoring with Sentiment Analysis

Sentiment analysis is often used to get a 360-degree view of how your customers and other important people think about your brand, product, or company. Product reviews and social media can tell you a lot about the strengths and weaknesses of your business. Companies can employ sentiment analysis. Companies such as Unamo can also use sentiment analysis to determine how a new product, an ad campaign, or a customer’s response on social media to recent news about the company impacts sales or customer satisfaction. Customer service representatives frequently use sentiment or intent analysis to classify incoming customer emails as “urgent” or “not urgent” based on how the email makes the customer feel. This allows them to identify dissatisfied customers prior to contacting them. The agent then prioritizes the resolution of the user’s most urgent requests. As machine learning makes customer service more automated, it is becoming increasingly important to comprehend each case’s disposition and objective.

Market Research and Analysis Using Sentiment Analysis

In business intelligence, sentiment analysis is used to find out why consumers do or don’t like something (e.g., why do people buy a certain product? What are their thoughts on the user experience? Did the customer service match their expectations?)

Sentiment analysis can also be used in political science, sociology, and psychology to, among other things, look at patterns, ideological biases, opinions, and reactions. Many of these applications are already operational. Bing’s Multi-Perspective Answers solution now includes sentiment analysis. Hedge firms very likely use the technology to forecast price movements based on public mood. CallMiner, for example, provides sentiment analysis for client interactions as a service.

Question Answering

Question answering (QA) is a subfield of artificial intelligence that combines natural language processing and information retrieval to make systems that can answer people’s questions in their own words. By querying a knowledge base, which is a structured database of facts, or a collection of unstructured natural language documents, question-answering computers can locate responses. There exist both closed-domain (responding to requests from a single domain) and open-domain question-answering systems (relying on general ontologies and widespread knowledge).

Watson by IBM is an illustration of the latter type of quality assurance system. Open-domain answering systems convert questions posed in natural language into structured queries. Keyword extraction is used to establish the inquiry type (who, where, how many). Part-of-speech tagging and syntactic parsing methodologies (person, place, number) can be used to determine the sort of response. The search terms are then entered into an information retrieval system. The response is then transformed into comprehensible text by parsing.