Sentiment Analysis in Machine Learning

Sentiment Analysis in Machine Learning

Sentiment Analysis is on the roll and why should’nt it be ? Every company and business is focussed on understanding what people think and feel about a product or service. The only difference is that now instead of people it is a software or an intelligent machine deducing these emotions. Sentiment analysis can be considered on the foreground for businesses, as it uses Natural Language Processing, text analysis & Statistics to extract important sentiments into majorly three categories i.e.positive, neutral and negative.

What do we know about sentiment analysis?

Sentiment analysis also known as opinion mining, is all about extracting information from commercial interests and growing research data, which is basically unstructured. One can clearly understand that machines becoming intuitive about deducing the tone of a particular write up can be fairly difficult. Contextual understanding is complex and expecting a machine to get hold of it is one heck of a level to achieve. For instance, consider a statement: “My flight has been cancelled, great!”. It is easy for a human to deduce that it is a negative response but machine might consider it positive due to the word “great”.

Rainfall Impact on Commodity Pricing

“According to a survey in sciencedirect for two long surveys on sentimental analysis presented by Lee, Pang and Liu that discusses the challenges and its applications. It also mentions the solution to all the problems.

Importance of sentimental analysis in making corporate decisions

It is vigorously growing as an important facet of business world. It can be used for various purposes.

Proper estimations – This process applies the text technique and NLP that is, Natural Language Processing that will help in identifying and extracting apt information from the available data. This data can be used to calculate or estimate emotions, attitudes and even opinions to make good corporate decisions.
According to a study presented by ieeexplore for sentiment analysis of a news article done by SenticNet and ConceptNet, “It provides 71% accuracy in classification, 59% positive and 91 % of precision for neutral sentences.”

Better understanding of the scenario – with developing business prospects, the need to understand customer is also growing. There has been a steady increment in the interests from various brands. Todays’ business world looks toward data analytic streams and business insight for better response.
As per a recent study by Google Trends, “ sentiment analysis has grown over time.”

Thorough market research – Sentiment analysis is known to provide accurate data regarding the market. Using various techniques, it takes all the details into account to provide a thorough study of the corporate sector, which can help people to make accurate business decisions.
“As per a recent study by Zendesk, “Around 45% of customers have bad experience of customer service and just 30% of customers have good experience.”

Wrapping it up

Though the technology is in its infacy currently, it is surrounded by coveats too. The limitation of showing results in a single dimension can question its accurate prediction. Undoubtedly, the algorithm is powerful and the technology is capable of compiling best opinions, a multi-dimensional based prediction would be more apt in the near future.