Sentiment analysis (also known as opinion mining or emotion AI) is a field of Natural Language Processing (NLP) focused on identifying and extracting subjective information from text or audio, such as opinions, feelings, judgments, and emotions. It is mainly used to gain insights from large sets of unstructured data, such as customer reviews, social media posts, and online comments.
Sentiment analysis algorithms often use supervised learning techniques, wherein a dataset is used to train the algorithm and once it is trained, it can be used to detect sentiment from text. The model usually comprises of two parts: feature extraction and sentiment classification.
Feature extraction extracts features from the input text, such as punctuation marks, word embedding from a pre-trained model, part-of-speech tags, and sentiment understanding of the words in the text. These features are then used to train a supervised learning model, which predicts the sentiment of the text. The sentiment classification part of the algorithm determines the sentiment of the text, i.e. whether it is positive, negative, or neutral.
Sentiment analysis has many applications in the fields of marketing, customer service, and public opinion analysis. It is used to quickly categorize customer feedback so that companies can respond effectively to customer satisfaction surveys, customer reviews, and other customer feedback. It is also used to analyze market trends and identify customer preferences.
Overall, sentiment analysis is an important tool for companies to gain insights from vast amounts of unstructured text data. By using sentiment analysis algorithms, they can identify customer sentiment and make sure that customer feedback is heard.
