Sentiment Analysis
Sentiment analysis, also known as opinion mining, is the analysis of attitudes, and emotions derived from online opinions. The role of feelings is often overlooked when analyzing data; however, it could reveal hidden messages. Compared to solely looking at numbers, sentiment analysis takes the next step by focusing on the subjective side of data. With this information, businesses can more accurately understand how and why customers behave in certain ways and figure out mitigation strategies based on up-to-the minute information. But the real question is, how does one measure sentiment?
Comments, shares, likes, and re-tweets are quality metrics useful in evaluating consumer engagement from his or her opinions. To perform sentiment analysis, natural language processing (NLP) tools are used to deal with the actual, human text (opinions) for computers and machines to be able to read. Artificial intelligence can be derived using NLP and certain algorithms to determine whether something is negative, positive, or neutral. This is used for data mining, which is a process used to find human-interpretable patterns and describe the data.
In data mining, sentiments can be classified by clustering into sentiment groups. Sentiment analysis software can score an individual’s words, phrases, or entire posts to be put into positive, negative, or neutral groups. Additional granularities may be added, however, it would reduce scoring accuracy. A basic example is if someone writes a tweet saying, “I love chocolate”; this would be scored as positive. If a positive phrase and negative phrase were in the same sentence, this would be scored neutral. There are many practical monitoring tools out there to track user sentiments, such as Google Analytics, Google Alerts, Radian 6, etc.
Although sentiment analysis is an important consideration in analyzing unstructured data, the only way to measure its credibility and accuracy is by comparing data to basic psychology. Understanding the way humans think is still too complex for computers to interpret. Even though this process requires further development, sentiment analysis takes a new direction in text mining and can be useful for companies in areas of improvement and development.
Works Cited
Rouse, Margaret. “What Is Opinion Mining (sentiment Mining)? – Definition from WhatIs.com.” SearchBusinessAnalytics. TechTarget, n.d. Web. 2015.
“Sentiment Analysis Helps Predict Where a Market Is Heading.” Market Research Bulletin, 3 Sept. 2013. Web. 2015.
“What Is Sentiment Analysis?” Semantria. Lexalytics, n.d. Web. 2015.