It’s imperative to have a sufficiently sophisticated and rigorous enough approach that relevant context can be taken into account. It’s an incredibly difficult issue, and sarcasm and other types of ironic language are inherently problematic for machines to detect when looked at in isolation. Analyzing natural language data is, in my opinion, the problem of the next 2-3 decades. Russell continued, “As to sarcasm, like any other type of natural language processing (NLP) analysis, context matters. Also, since sentiment very likely changes over time according to a person’s mood, world events, and so forth, it’s usually important to look at data from the standpoint of time.” Advantage: Simulation offers calibration of the entire market, not just one brand. Here are the seven you need to know about: 1.
Like with any business tool, there are both advantages and disadvantages to simulation. With a large enough sample, outliers are diluted in the aggregate. The complete advantages and disadvantages of simulation. An individual’s sentiment toward a brand or product may be influenced by one or more indirect causes someone might have a bad day and tweet a negative remark about something they otherwise had a pretty neutral opinion about. No particular data point is necessarily relevant. It’s critical to mine a large - and relevant - sample of data when attempting to measure sentiment. When asked about the limitations of sentiment analysis, Russell said, “Like all opinions, sentiment is inherently subjective from person to person, and can even be outright irrational. The extremes on the spectrum usually correspond to positive or negative feelings about something, such as a product, brand, or person.”
Russell states, “Think of sentiment analysis as “opinion mining,” where the objective is to classify an opinion according to a polar spectrum. A recent interview with Matthew Russell, co-founder and Principal of Zaffra discusses the limitations and possible applications of sentiment analysis.
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