What is Sentiment Analysis?

Sentiment Analysis is 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 also extract the attributes of the expression.

Different levels of scope for Sentiment Analysis are

• 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.

How does Sentiment Analysis work?

There are many methods and algorithms to implement sentiment analysis systems, which can be classified as:

• Rule-based systems that perform sentiment analysis based on a set of manually crafted rules.
• Automatic systems that rely on machine learning techniques to learn from data.
• Hybrid systems that combine both rule based and automatic approaches.

Types of Sentiment Analysis are

• Fine-grained Sentiment Analysis
• Emotion detection
• Aspect-based Sentiment Analysis
• Intent analysis
• Multilingual sentiment analysis

Usecases and Applications

• Social media monitoring
• Brand monitoring
• Voice of customer (VoC)
• Customer service
• Workforce analytics and voice of employee
• Product analytics
• Market research and analysis

Sentiment Analysis Challenges

• Subjectivity and Tone
• Context and Polarity
• Irony and Sarcasm
• Comparisons
• Emojis
• Defining Neutral