In this article, you will learn about text mining social media for competitive analysis. Social media is a treasure trove of brand and consumer insight. However, with the vast amount of data around you need robust analysis and an excellent search capability to uncover the hidden gems.
What is Competitive Analysis?
Competitive Analysis the identification of your competitors and evaluating their strategies to understand their strengths and weaknesses compared to your own product or service.
A text mining tool allows you to develop Competitive Analysis from social media. Text analytics derives information from text sources. And is able to analyse any text-based datasets. These datasets can include surveys, posts, support tickets, review sites and social media. Text analysis is getting very good at translating how we actually write and taking into account our nuances, idiosyncrasies and subjectivity.
Beyond what and how
Now, you can use text analysis within social media to answer a number of questions associated with your products, brands, companies, competitors or even the size of Kim Kardashian’s backside. Text analysis does not just tell what is being said, it is also able to how it is said. By understanding the sentiment and emotions expressed within the words. Text analysis goes beyond the the what and how and works out what the percentage of the conversation is about your brand, topic or product. By identifying the main topics, words, and phrases what is been said it can also tell how negative or positive the public sentiments are.
And you also need to understand what is driving the sentiment and tone of the content. Also, how the tone changes over time and then detect the intent to purchase your product or service.
In fact all the stages of your buying cycle can be detected. Text analysis over time is able to answer any questions you wish to ask and providing the is data available, with the help of Machine Learning it will help create your own categories and teach your platform to be able to sort social posts into categories. There are two main approaches to text analytics. Machine learning models and linguistic rules.
Text analysis based on Machine Learning naturally isolates patterns from text. Statistical methods are used to find the most important and useful patterns for the desired behaviour. But Machine learning analysis methods are diverse but they learn what are the most distinctive and valuable and patterns. For the moment however, Machine Learning still needs human input.
Rule-based pattern collating are usually boolean based keyword chains or more complicated models developed over time by language experts. The linguistic rules capabilities range from finding parts of speech, syntax, as well as the inflections and stylistic variations and rules regarding your various topics.
All types text analysis have their strengths and weaknesses. But the flexibility of being able to switch between the two models will provide better results.
In this article, you learned about text mining social media for competitive analysis.