A beginners guide to Sentiment Analysis

 Sentiment analysis is the automated process of understanding an opinion about a given subject from written language. It is a classic example of machine learning.

In a world where we generate 2.5 quintillion bytes of data every day, sentiment analysis has become a key tool for making sense of that data. This has allowed companies to get key insights and automate all kind of processes. Sentiment Analysis also known as Opinion Mining is a field within Natural Language Processing (NLP) that builds systems that try to identify and extract opinions within text. Usually, besides identifying the opinion, these systems extract polarity of the expression, for example, if the writer expresses a positive or negative opinion.

Why Sentiment Analysis is a topic of great interest

Currently, sentiment analysis is a topic of great interest and development since it has many practical applications. Since publicly and privately available information over Internet is constantly growing, a large number of texts expressing opinions are available in review sites, forums, blogs, and social media.

What we can perform by using sentiment analysis

With the help of sentiment analysis systems, this unstructured information could be automatically transformed into structured data of public opinions about products, services, brands, politics, or any topic that people can express opinions about. This data can be very useful for commercial applications like marketing analysis, public relations, product reviews, net promoter scoring, product feedback, and customer service.

Subjectivity and Polarity Classification

Sentiment analysis, just as many other NLP problems, can be modeled as a classification problem where two sub-problems must be resolved. Classifying a sentence as subjective or objective, known as subjectivity classification. Classifying a sentence as expressing a positive, negative or neutral opinion, known as polarity classification.

Sentiment analysis can be applied at different levels of scope:

• Document level sentiment analysis obtains the sentiment of a complete document or paragraph.
• Sentence level sentiment analysis obtains the sentiment of a single sentence.
• Sub-sentence level sentiment analysis obtains the sentiment of sub-expressions within a sentence.

It’s estimated that 80% of the world’s data is unstructured and not organized in a pre-defined manner. Most of this comes from text data, like emails, support tickets, chats, social media, surveys, articles, and documents. These texts are usually difficult, time-consuming and expensive to analyze, understand, and sort through. Sentiment analysis systems allows us to make sense of this sea of unstructured text by automating the processes, getting actionable insights, and saving hours of manual data processing, in other words, by making teams more efficient.