Table of Contents
Sentiment analysis involves utilizing natural language processing and machine learning methods to identify and extract subjective information from text data.
Analyzing a text’s tone, attitude, and emotion is used to identify whether it is positive, negative, or neutral.
Sentiment Analysis Types
Fine-grained analysis of sentiment:
This is contingent upon the polarity root. This category can be characterized as extremely positive, positive, neutral, negative, or extremely negative. The rating scale could range from 1 to 5. If the rating is 5, it is considered positive; if it is 2, it is negative; and if it is 3, it is neutral.
The emotions joyful, sad, angry, upset, sociable, pleasant, and so forth fall under emotion detection. It is also referred to as the lexicon approach to sentiment analysis.
Aspect-based sentiment analysis:
It focuses on a specific aspect, such as the battery, screen, and camera quality of a mobile phone, when a user wishes to examine its features.
Multilingual sentiment analysis:
It is necessary to classify multilingual languages as positive, negative, and neutral. This is extremely challenging and relatively challenging.