Sentiment analysis is the process of classifying whether a block of text is positive, negative, or, neutral. Sentiment analysis is the contextual mining of words that indicates the social sentiment of a brand and also helps the business to determine whether the product they are manufacturing is going to make a demand in the market or not. The goal which Sentiment analysis tries to gain is to analyze people’s opinions in a way that can help the businesses expand. It focuses not only on polarity (positive, negative & neutral) but also on emotions (happy, sad, angry, etc.).
OBJECTIVE: To analyze the sentences for determining emotions like :
o Texts containing positive emotions; happiness, amusement or joy,
o Texts containing negative emotions; disappointment, anger or disappointment,
o Texts containing neutral emotions.
This is done by using supervised learning techniques such as Naive Bayes, Support Vector Machines and Long Short-Term Memory (LSTM) algorithm.
It’s an important application that could serve as a tool to help businesses monitor brand and product sentiment in customer feedback, and understand customer needs. The analysis helps spot negative or positive sentiments about their product or service with precision, and take necessary steps to address those areas.