The Big Question:
Is it possible to analyze the social media and identify the sentiment for any new or existing product or service like phone, tablet or even a movie?
The Motivation
There are a lot of review sites for each new product or even existing products. Many times reading the content of the review; it becomes evident that the content is paid and targeted to promote the product instead of giving a realistic picture. If we can analyze twitter content or other social content sites life FaceBook, YouTube or Linkedin, it will be fun but also valuable. This idea is a motivation enough to start learning and increasing the knowledge in field Business Analytics. The social media is very potent source of Big Data; there have been many analytics-driven campaigns already done on social media for various events in the past.
The Business Problem
There is a lot of data with organizations mainly from social media feeds; blogs, websites and other sources like mobile that mostly stays unused. Business Intelligence and big data analytics have become increasingly important in the business communities. Companies catering to customers have long used data to segregate and target customers. Big data analytics permits a major step beyond segregation and segmentation. An effective way to utilize the data is required.
The Opportunity
Next-generation product owners can track the behavior of an individual customer. They can recognize when the customers are finalizing their purchase decision. A nudge to complete transaction by bundling preferred products or a single product might close the sale. Retailing is the obvious business that benefits from this service directly (Russom). An application that can analyze its products sentiments based on twitter and social media data can be the most sought after tool.
The social media have come up as the best source of tips and experience on any product or service. Data analysis can see patterns of influence and peaks in communication on a product by utilizing the hashtags and cloud. The online data is also a real-time behavior for a large number of people. Organizations must start by asking the correct questions with respect to their products and the desired insights. The questions must meet the Big Data business objectives, and then the next process is analytics on that data. Implementing analytics by first finalizing the desired outcomes, insights, would lead to better results (LaValle).
Using readily available data for providing sentiments and feedback on newly launched products would give the business organizations a big boost. This would also be beneficial for the consumers as they would have a chance to see real-data and real-insights instead of some paid or pseudo-advertising.
Discussion: Benefits and Drawbacks
Novel tools and methods are used to embed information into business processes like use cases, optimization, analytics solutions, simulations and workflows are making insights more understandable and actionable. Trend analysis, standardized and forecasting are the most important tools in current times.
The solution of creating a sentiment analytic application for products based on social media can become the most sought after tool. As discussed in the above section, most of the organizations are working on Big Data and Analytics. Most of them already have or plan to have in future a policy for Big Data. This means that the trend will keep up, and eventually each company will have their similar product.
Big Data has its perils. With such large data sets and fine-grained measurement, the risk of “false positives” always lingers. Much of the information might seem valuable, however it could be junk that is of no use (Lohr).
However, despite the caveats, there is no turning back. Data is at the forefront and demanding huge attention and action from various businesses. The solution of creating a data analytics application can be the most apt for solving the problem of authentic product feedback by analyzing social media content.
Work Cited
LaValle, Steve, et al. "Big data, analytics and the path from insights to value."
MIT Sloan Management Review 21 (2013).
Russom, P. (2011). Big data analytics. TDWI Best Practices Report, Fourth Quarter.
Lohr, S. (2012). The age of big data. New York Times, 11.
