Sentiment analysis determines the emotional tone behind the usage of a series of words in order to analyze the opinions and emotions behind an online mention.
Sentiment analysis started getting traction in the year 2010 and now it is expanding and has become an influential tool to such an extent that it is being dignified as a field of study. However, this young science is still to be used with caution. Larger the collected data about the audience, greater will be the chances of the success of your analysis.
Performing sentiment analysis
In social media listening, there are three classification algorithms used predominantly for sentiment analysis:
· Naive Bayes
· Decision Trees
Each of the above listed classification algorithms has its own pros and cons. However, Naïve-Bayes is considered as the most accurate one of these.
There are two algorithms mainly used alongside a lexicon based approach:
The best approach for sentiment analysis in social media is a combination of both these algorithms. However, the most commonly used machine learning algorithms is the Naïve-Bayes algorithm.So, let us learn what is the Naïve-Bayes classifier?
Naïve-Bayes algorithm is a machine learning classification algorithm through which each element is individually valued in order to determine a probability that the sum of the values will deliver a pre-defined outcome.
Here’s an example:
Any fruit can be considered as an apple if it is round in shape, red in color and about 3 inches in diameter. These features are interdependent and also they depend on the existence of various other features. All of these properties collectively contribute to the possibility that the given fruit is an apple. And thus, it is known as “Naïve”.
In case of social media sentiment analysis, Naïve-Bayes classifier is used in order to determine if a particular social mention for any event is sentiment-wise positive, negative or neutral.
Naïve-Bayes first requires having a dataset. Textual sentiment analysis usually arrives in the form of a set-of-words already sorted out in positive and negative sentiment categories.
As stated earlier, no social media sentiment analysis algorithm is perfect. Just the way humans don’t get it right all the time; the machines can’t be expected to do it either. Even in case the bag-of-words are categorized in the best possible correct way, contextually it is not possible to justify the categorization all the time.
Still, for any new campaign categorized positive and negative keywords help in sentiment monitoring and this in turn, helps in molding the event/brand strategy accordingly.
However, there are various other benefits of sentiment analysis through social media:
Benefits of sentiments analysis
Sentiment analysis has become quite a useful concept in social media monitoring. It allows gaining an overall view of a wider a public opinion behind any particular campaign or event. Different social media monitoring tools and real time analytics capabilities make the process quicker and easier than ever before.
The application of sentiment analysis in social media is quite broad and powerful. Extracting insights from the data available on social media platforms is being widely adopted by different organisation across the world owing to the fact that sentiment analysis helps them in brand management. It helps in understanding the audience opinions in order to outline their brand/campaign/product strategy accordingly. This not only helps them to comprehend the reactions of their audience but also in measuring the ROI of their marketing campaign and curating a concrete plan for their future campaigns. It also helps building a good customer support service.Sentiment analysis also help is quickly understanding the consumer attitudes and react accordingly in order to modify public opinion and campaigns.
Sentiment analysis as a powerful tool
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