What is Sentiment Analysis?
Did you know that you can automatically direct your social media mentions to team members that are most suitable to respond? Let me explain.
Suppose you (an entrepreneur) have recently launched a new digital product and you really want to find out your users’ opinion about your product. Considering the population of people on social media, your customers are definitely there and they would likely be talking about you. Twitter might be a good place to look for your product mentions.
As a proactive entrepreneur, You have logged on Twitter to search for related keywords to your product, and right to your face you see thousands of search results. You probably have thought about a way to collect all of these results and maybe perform some sorts of analysis on them…meanwhile scrolling through the search results you notice some words are very consistent, you know the most suitable team member(s) to address these concerns, yet you are not sure how to make sense of these texts!
You don’t want to leave all these opinions bare because the pen they say is mightier than the sword and with today’s internet technology, such opinions can either make or mar your business.
As with every problem in life, there is a solution! You can automatically detect the number of reviews, and the message behind the reviews. You can even direct such reviews to the most suitable team member(s) to address the opinion. For instance, you can direct a Twitter mention to your software design team for design related mentions in order to apply the appropriate action necessary for the mention.
Spoiler alert, this is not a twitter enhanced feature!
It is a section of the natural language processing field that is used to analyze text-based data. The description above is only one of the several applications of Sentiment Analysis. It is also popularly referred to as opinion mining.
With the outburst of text related data via blogs, websites, news platforms, social media, and the various opinion sharing platforms, internet users are continuously putting out reviews, opinions and discussing topics within the ecosystem such as movie reviews, hotel reviews, news stories, and even elections.
Sentiment analysis helps make sense of unstructured text data, after all, 80% of the world’s data is unstructured. It is therefore important that we maximize tools like Sentiment analysis to draw out insights from data. Sentiment analysis does not only identify opinions but also extract attributes of these opinions.
Sentiment analysis uses the level of polarity (by measuring the level of positivity, negativity, and neutrality in a set of texts), emotion detection (also called lexicon analysis) and intent analysis(used to determine the purpose behind a text) to bring valuable insights to text-based data.
It is built on 2 major approaches which include rule-based and automatic approaches. The rule-based approach matches words with preferred scores from a lexicon and applies these scores to the data set. The automatic approach uses machine learning technique by training a set of data to generate an algorithm, this algorithm is used to extract relevant knowledge from data. The rule-based approach is not sufficiently accurate as it’s a manual selection of keywords and it is not scalable. The automatic approach is considered to provide more accurate results and highly scalable, although it requires a training data set to form its algorithm.
Sentiment analysis can be applied across all sectors where human interaction is required, be it medicine, banking, the stock market, marketing, education, oil and gas. It’s a good way to put free available opinions to test and draw out interesting results.
Whenever you need to make sense of text-based data, think Sentiment Analysis, think Opinion Mining!
#sentimentanalysis #machinelearning #opinionmining #datascientist
PS: This post is a response to the challenge by Ibrahim Gana
