Opinion Mining is another name for Sentiment Analysis. Opinion Mining is the process of extracting opinions from text data. Opinion mining can also be used to detect the emotional tone of a piece of text. For example, a review that is angry in tone might be classified as negative, while a review that is happy in tone might be classified as positive.
This process can be done using a variety of methods, including natural language processing (NLP) and machine learning techniques.
Opinion Mining also is used to identify the opinion holder and the target of the opinion. For example, in a review of a restaurant, the opinion holder may be the reviewer, and the target of the opinion may be the restaurant itself.
Of Opinion Mining, NLP, and Machine Learning
Opinion Mining is closely related to both natural language processing (NLP) and machine learning.
NLP is a field of computer science that deals with the interaction between computers and human languages. NLP can be used for Opinion Mining in a number of ways. For example, NLP can be used to identify the sentiment of a piece of text, as well as to identify the opinion holder and the target of the opinion. NLP can also be used to automatically classify opinions into predefined categories, such as positive or negative.
Machine learning on the other hand Machine learning is a more general term that refers to any type of artificial intelligence that allows computers to learn from data. For example, a machine learning algorithm could be trained on a dataset of reviews, and then used to predict the sentiment of new reviews.
Opinion Mining vs Topic Modeling?
Opinion mining is a specific type of text mining that is used to extract opinions from text data. Topic modeling, on the other hand, is a method of machine learning that can be used to automatically discover the topics in a piece of text.
Both opinion mining and topic modeling can be used to understand the content of a piece of text.