How Sentiment Analysis Is Helping Companies Refine Their Marketing Efforts
It will come as no surprise for those who are part of the information economy – or who simply use the Internet on a daily basis that users and businesses are generating around 2.5 quintillion bytes of data every day- and the amount of data being generated is growing at an exponential speed. In fact, some research has suggested around 90% of the data available today was created in the last two years. The sheer amount of data being generated on a daily basis provides companies with a challenge. How do marketing professionals (or product analysts and other stakeholders for that matter) deal with that vast amount of data – and make sense of consumer feedback in a way that can help the business fine-tune the information (and product and service offerings) that they are sharing with their target audiences?
Fortunately, machine learning in coming to the rescue of companies like this through ‘sentiment analysis.’ Just what is this valuable tool – and why are an increasing number of companies now leveraging its power?
In short, sentiment analysis is an automated process whereby a company relies on machine learning and to a certain extent, artificial intelligence to sort data gleaned from the text on such platforms as social media, review sites, discussion threads, and even blogs. The information is automatically classified into three attributes: Polarity (positive or negative), the subject and elements of demographic analysis – i.e., who is expressing the opinions – and what are the demographic factors that may influence that opinion. The polarity report can further drill down into even finer detail – providing information on whether the sentiment is ‘Very positive, ‘Positive,’ ‘Neutral,’ ‘Negative’ or ‘Very Negative.’ Further information around emotional responses to communication can also be analyzed into positive feelings such as ‘happiness,’ ‘enthusiasm,’ or negative emotions such as anger or sadness.
Using this system, what could be termed ‘unstructured’ or’unfiltered’ opinion is transformed into useful data that can guide the stakeholder in future communication and other marketing outreaches. The stakeholders can vary from political parties, services providers, NGO’s or brand custodians (to name only a few). There is a myriad of different uses for this newly structured data. Public relations professionals use it to convey messages, marketing, and product analysts more effectively may use data to refine their communication around products and services, promotional companies use that data to improve their activities further and customer service advisors use such data in order to provide companies with valuable information on how best they can retain existing clients – and attract new customers.
The types of sentiment analysis can vary in focus, depending on the granularity of the source data. For instance, document-level analysis sorts sentiment expressed in an entire document, sentence-level analysis can obtain sentiment data from a single sentence. ‘Sub-sentence’ level analysis can drill down even further to obtain valuable data from stand-alone parts of a single sentence or the expressions used in a sentence.
It should be readily apparent that sentiment analysis has a huge scope for abuse. The individual’s right to privacy – and just how that data will be used are two problem areas that have already raised the eyebrows of many researchers. What is obvious is that companies and organizations will continue to use machine learning and elements of artificial intelligence to make sense of ever-growing amounts of data. It is a trend that shows no sign of slowing down – nor will it in a fast-changing and ever more competitive world.
For more information on sentiment analysis, visit https://goascribe.com/improve-sentiment-analysisfor an overview of its impact on an organization’s performance improvement efforts and processing times.