Sentiment Analysis: Bridging the Gap Between Machines and Emotions

It is safe to assume that the advent of internet communities has never been so intense as it’s now. With the sheer number of online users on various social platforms showing no hints of slowing down, this newfangled virtual cluster has provided businesses with a completely different insight into their target group’s perceptions.

This insight relates to the fact that almost every online social platform is used by people to express themselves. Right from daily happenings in life to discussing political issues, social platforms like Facebook and Twitter have provided people a space to share what’s on their mind with a larger audience than it was ever possible before. It is not uncommon for people to openly share their likes and dislikes, and businesses have now begun to knock on the doors of this potential goldmine.

The scope of analyzing customer’s perceptions about a particular product and service via tracking their online conversations has never been bigger. This process of gaining an insight about what your current and prospective customers think about your brand is called Sentiment Analysis.

How It Works

For small business whose audience size is limited, manually going through your posts, status updates, comments, etc., can prove to be sufficient. This can also be carried out using free online tools such as Weggener Edstrom and Social Mention. However, when it comes to larger entities, the use of sophisticated analytical tools is required to gauge the sentimental patterns. These include tools like Opinion Crawl and Lithium Customer Intelligence Center. These tools are equipped with an inbuilt algorithm that tracks the general pattern and trends of various emotions exhibited by a group of people across various social platforms, and provides the result in the form of a report based on the average sentimental values. These algorithms are continuously updated in line with the ever changing social sphere, but their accuracy still remains the most debatable issue surrounding Sentiment Analysis.

Drawbacks

The chief drawback of Sentiment Analysis is that its accuracy is limited to decoding expressions that fall within the purview of commonly used expressions and words. Hence, it is fine for tracking extreme reactions of people. However, the issue arises when subtle feedbacks are thrown up by online users that comprise a tone that is sarcastic, ironic or funny.  And if that isn’t enough, internet literature dwells from slangs and abbreviations, making it very difficult for an algorithm to decipher it.

The solution lies in moderation, and finding the right balance between using machines and humans. The most viable method is to carry out automated analysis and then follow it up with a quick manual one.

Another drawback is that since this method works with trends and patters, its accuracy is directly proportional to the sample size. When the target group is smaller, the resulting report may show a trend that is inaccurate. This can only be solved with time, as algorithms facilitating more advanced analysis are developed.

Conclusion

There is no doubt that Sentiment Analysis is the next big thing as far as business intelligence is concerned. Never before have businesses got the opportunity to drill down to their customer’s hearts as they have now. We can safely assume that once a more sophisticated model comes into play, this modish analysis would take the fields of marketing and public relations to another level.

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