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.
In social media listening, there are three
classification algorithms used predominantly for sentiment analysis:
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:
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.
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.
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”.
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
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.
for any new campaign categorized positive and negative keywords help in
sentiment monitoring and this in turn, helps in molding the event/brand
there are various other benefits of sentiment analysis through social media:
of sentiments analysis
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
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 as a powerful