Introduction to Sentimental Analysis

Sentiment analysis is the field of study which deals with responses as well as feelings, which is generated from textual data, being extensively used in fields like web mining, and social media analytics because sentiments are the most essential characteristics that is used to judge the human behaviour.

It is usually performed on unstructured textual data to help businesses monitor product sentiment in customer feedback, and understand what customer need. By automatically analyzing customer feedback, that is, opinions in survey responses or social media conversations, brands learn what makes customers happy or frustrated. They can thus provide products and services to meet their customers’ needs.

Sentimental Analysis

Sentimental Analysis or Opinion mining works based on natural language processing (NLP) and machine learning algorithms, which automatically determines the emotional tone behind each textual reviews. Sentiment analysis is extremely important because it helps businesses understand the overall opinions of their customers. By sorting the sentiments, social media conversations etc., you can make faster and better decisions with accuracy.

A rule-based system works based on a set of man-made rules to identify subjectivity, polarity, or the subject of an opinion. These rules may include various NLP techniques developed in computational linguistics, such as Stemming, tokenization, part-of-speech tagging and parsing or Lexicons, i.e. lists of words and expressions.

How a rule-based system works:

It defines lists of words e.g. negative words like bad, worst, ugly, etc and positive words like good, best, beautiful, etc.

It could count the number of positive and negative words that appear in the data.

If the number of positive word appeared is greater than the number of negative word appeared, the system returns a positive sentiment, and vice versa. If these numbers are even, the system will return a neutral sentiment.

The applications of sentiment analysis are unlimited, and they can be applied to any industry, which extends from finance and retail to hospitality and technology. Applications include Social Media Monitoring, Brand Monitoring, Voice of customer, Customer Service and Market Research to name a few.

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