My definition: Sentiment analysis is the effort to systematically detect and evaluate opinions, attitudes, and emotions in a spectrum of personal, online, social, and enterprise information sources. Sentiment sources range over the spectrum of content types -- images, audio, video, and text -- and extend to transaction records that can be mined for sentiment-indicating behaviors.
Seen from a broad perspective, sentiment analysis involves content analysis and the sort of number crunching, visual data exploration, and results delivery that is immediately familiar to anyone versed in data mining and business intelligence. Seen from this broad perspective, sentiment analysis draws on, but is not a subset of, text analytics. Text analytics does help you get at sentiment in textual sources, but there are many more sentiment sources out there than just text. Further, as we'll see, you don't even necessarily need text analytics to get at sentiment in text.
If sentiment analysis were a text analytics subset, then a smile, yelling, an angry gesture, and dwell-time on a Web page would all count for nothing. Yet they don't count for nothing; they contain personal and business value. They express mood, attitude, and emotion that are conveyed visually, audibly, and via movement, but they're non-textual and thus can't be parsed directly via text analytics.
Sure, you can transcribe speech to written text and describe an image in words, but you’ll incur loss of context and fidelity and, hence, lower analytical accuracy. Better to pull data from these sources in their native forms. It can be done: A $59 consumer-grade camera can detect a smile. Leading-edge call-center solutions detect emotion by modeling volume, pace, intonation of speech. Images and speech, mined for mood and emotion, are non-textual -- so no, again, sentiment analysis is not a subset of text analytics.
My four examples illustrate non-textual ways humans communicate. The first three, a smile, yelling, and an angry gesture, directly convey mood and emotion. The fourth, dwell-time on a Web page, signals interest and intent, as do purchases and other actions that aggregate to behavior patterns. Survey responses, consumer and business spending trends, commercial inventories: Economists and business forecasters have inferred economic sentiment from these measures for decades. These methods do not involve text analytics.
Finally, crowdsourcing is an important technique that applies human judgment to larger-scale tasks. Want to classify 200,000 photos? No problem! Provider examples include CrowdFlower and Crowd Control Software (layered on Amazon Mechnical Turk). They control the evaluation process, ensuring consistency and quality. Human text/content assessment is not typically classed as “text analytics,” but when those assessments are systematized to assess opinions, attitudes, and emotions, they definitely constitute sentiment analysis, further supporting that sentiment analysis is not a subset of text analytics.
Text analytics is great stuff, but it’s not the be-all and end-all of sentiment analysis!