Sentiment Analysis is the use of natural language processing, statistics, and text analysis to extract, and identify the sentiment of text into positive, negative, or neutral categories. We often see sentiment analysis used to arrive at a binary decision: somebody is either for or against something, users like or dislike something, or the product is good or bad.
Sentiment analysis is also called opinion mining since it includes identifying consumer attitudes, emotions, and opinions of a company’s product, brand, or service.
Sentiment Analysis Use Cases
The use of sentiment analysis is frequently applied to reviews and social media to help marketing and customer service teams identify the feelings of consumers. In media, such as product reviews, sentiment analysis can be used to uncover whether consumers are satisfied or dissatisfied with a product. Likewise, a company could use sentiment analysis to measure the impact of a new product, ad campaign, or consumer’s response to recent company news on social media.
A customer service agent at a company could use sentiment analysis to automatically sort incoming user email into “urgent” or “not urgent” buckets based on the sentiment of the email, proactively identifying frustrated users. The agent could then direct their time toward resolving the users with the most urgent needs first.
Sentiment analysis is often used in business intelligence to understand the subjective reasons why consumers are or are not responding to something (e.g. Why are consumers buying a product? What do they think of the user experience? Did customer service support meet their expectations?). Sentiment analysis can also be used in the areas of political science, sociology, and psychology to analyze trends, ideological bias, opinions, gauge reactions, etc.
Challenges of Sentiment Analysis
People express opinions in complex ways, which makes understanding the subject of human opinions a difficult problem to solve. Rhetorical devices like sarcasm, irony, and implied meaning can mislead sentiment analysis, which is why concise and focused opinions like product, book, movie, and music reviews are easier to analyze.
Sentiment Analysis Algorithms
Veltrod provides several powerful sentiment analysis algorithmsn to developers. Implementing sentiment analysis in your apps is as simple as calling our REST API. There are no servers to setup, or settings to configure. Sentiment Analysis can be used to quickly analyze the text of research papers, news articles, social media posts like Tweets, and more.
Social Sentiment Analysis is an algorithm that is tuned to analyze the sentiment of social media content, like tweets and status updates. The algorithm takes a string, and returns the sentiment rating for the “positive,” “negative,” and “neutral.” In addition, this algorithm provides a compound result, which is the general, overall sentiment of the string.
- Posted by Udit Agarwal
- November 11, 2017