Sentiment analysis is a subset of the discipline of national language processing (NLP). Namely, it is the technique of identifying human emotions from written text and it has become a highly-popular mode of technology. “We humans are passionate creatures and emotions ooze out of our actions, spoken language and written words. Techniques that dissect the human language to decipher underlying emotions are in huge demand,” said Ganes Kesari, co-founder and head of Analytics at Gramener.
To better understand sentiment analysis, though, a deeper description of NLP and what it can do is in order.
NLP attempts to automate and analyze large amounts of natural language data. This data is normally unstructured and ambiguous, adding to the complexity of the task, said Brandon Haynie, chief data scientist at Babel Street. “One can see how difficult it would be to power a system that can account for all the language nuisances with all its inconsistencies that humans process effortlessly. Great strides from the initial rule-based techniques to the more popular machine learning with its roots in statistical models continue to push the capabilities of NLP.”
Sentiment analysis, Haynie continued, dives further into language where it attempts to discover and classify opinion from subjective commentary. Terms or structures within statements such as “dislike,” “love,” “great” and “approve” all express a bias which in turn can be measured. “Therefore, when applying both NLP and sentiment analysis, not only would one understand the context of a statement, but they can recognize the feelings expressed by the author,” Haynie said.
“This becomes a powerful tool to analyze consumer reviews, commentary, social media, workplace environments and customer support.” Further granularity can reveal emotion to determine if a negative sentiment is based on anger or sadness and even the intent of a user when purchasing a product or voting on a particular topic, he concluded.
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A Boon for Marketing
Not surprisingly then, sentiment analysis is immensely valuable to brands, especially those in the public eye, said Stephen Blum, CTO and co-founder of PubNub. “Hooking sentiment analysis into social media, businesses can analyze how consumers are feeling about their brand on a massive scale — intelligently process tweets, Facebook mentions, forum entries, and more. [Businesses can] gauge the overall sentiment towards their brand overall, product launches, news, and more.”
For large enterprises, sentiment analysis helps data analysts ascertain public opinions, monitor their brand and product reputation, understand customer experiences and conduct nuanced market research, Aaina Bajaj, digital marketing specialist at Signity Solutions, noted.
Sentiment analysis, however, is not foolproof, said Megan Carrigan, strategy director at the digital agency Union, more development may be necessary for it to understand how context applies to the changing landscape of slang and how people use language. “For example, if a Twitter user said ‘Man, this beer is sick’ would the AI be able to infer that this is a positive sentiment even though its using negative language? You ultimately have to dig into the data a bit to feel if the sentiment is being reflected properly — especially if something unexpected pops up.”
Indeed, every customer-facing business is being discussed on so many forums that it is crucial to be able to monitor what is being said about you, what is the overall sentiment and what is driving that sentiment, said Brad Null, chief data scientist of Reputation.com. “So with that in mind, sentiment analysis generally exists alongside other components, such as topic and entity detection.”