Sentiment Analysis Sentiment analysis is a natural language processing (NLP) technique used to determine whether data is positive, negative or neutral. Sentiment analysis is often performed on textual data to help businesses monitor brand and product sentiment in customer feedback, and understand customer needs. Sentiment analysis is the process of detecting positive or negative sentiments in text. It’s often used by businesses to detect sentiment in social data, gauge brand reputation, and understand customers.
Why perform Sentiment Analysis? According to the survey,80% of the world’s data is unstructured. The data needs to be analyzed and be in a structured manner whether it is in the form of emails, texts, documents, articles, and many more. Sentiment Analysis is required as it stores data in an efficient, cost-friendly. Sentiment analysis solves real-time issues and can help you solve all the real-time scenarios.
Types of Sentiment Analysis
Fine-grained sentiment analysis: This depends on the polarity based. This category can be designed as very positive, positive, neutral, negative, or very negative. The rating is done on a scale 1 to 5. If the rating is 5 then it is very positive, 2 then negative and 3 then neutral.
Emotion detection: The sentiment happy, sad, angry, upset, jolly, pleasant, and so on come under emotion detection. It is also known as a lexicon method of sentiment analysis.
Aspect-based sentiment analysis: It focuses on a particular aspect like for instance, if a person wants to check the feature of the cell phone then it checks the aspect such as battery, screen, and camera quality then aspect based is used. Multilingual sentiment analysis: Multilingual consists of different languages where the classification needs to be done as positive, negative, and neutral. This is highly challenging and comparatively difficult.
Applications Sentiment Analysis has a wide range of applications as: 1...Social Media: If for instance the comments on social media side as Instagram, over here all the reviews are analyzed and categorized as positive, negative, and neutral. 2…Customer Service: In the play store, all the comments in the form of 1 to 5 are done with the help of sentiment analysis approaches. 3….Marketing Sector: In the marketing area where a particular product needs to be reviewed as good or bad. 4...Reviewer side: All the reviewers will have a look at the comments and will check and give the overall review of the product. Sentiment Analysis Challenges Sentiment analysis is one of the hardest tasks in natural language processing because even humans struggle to analyze sentiments accurately. Subjectivity and Tone