What's the meaning of Sentiment Analysis and Opinion Mining

In the last class, the teacher said something about Sentiment Analysis and Opinion Mining. In order to know clearly about it, I read some books and look up some information about it.

We always chat with each other to exchange opinions, which means our beliefs and knowledge are expressed externally through our opinions, they are central to almost all human activities and are key influencers of our behaviors. In order to be clearly about people’s opinion, we need to do sentiment Analysis, which means we need to do computational study of opinions, sentiments, appraisal, and emotions expressed in text. It is also known as Opinion Mining. We always express our opinion on blogs, discussions, microblogs and social network.

Sentiment analysis could help us do a lot of surveys. For this reason, when we need to make a decision we often seek out the opinions of others. This is not only true for individuals but also true for organizations.

From this picture, we can know what kinds of people are willing to purchase what kinds of things.

In order to make Sentiment Analysis, we have to do holder detection, target recognition and sentiment classification. The basic task of opinion mining is polarity classification. Polarity classification occurs when a piece of text stating an opinion on a single issue is classified as one of two opposing sentiments. Reviews such as “thumbs up” versus “thumbs down,” or “like” versus “dislike” are examples of polarity classification. Polarity classifications also identify pro and con expressions in online reviews and help make the product evaluations more credible. But we always meet some problems, if people can not explicitly express emotional states, and therefore the tools can’t capture a reviewer’s implicitly expressed opinion or sentiment.

Finally, we found some evolution of opinion mining. Currently, opinion mining and sentiment analysis rely on vector extraction to represent the most salient and important text features. We can use this vector to classify the most relevant features. Two commonly used features are term frequency and presence.

The Web has changed from “read- only” to “read-write.” This evolution created enthusiastic users interacting and sharing through social networks, online communities, blogs, wikis, and other collaborative me- dia. Collective knowledge has spread throughout the Web, particularly in areas related to everyday life, such as commerce, tourism, education, and health. Despite significant progress, however, opinion mining and sentiment analysis are still finding their own voice as new interdisciplinary fields. Recent approaches aim to better grasp the conceptual rules that govern sentiment, as well as the clues that can convert these concepts from realization to verbalization in the human mind. Future opinion-mining systems need broader and deeper common and commonsense knowledge bases. More complete knowledge must be combined with reasoning methods that are more deeply inspired by human thought and psychology. This will lead to a better understanding of natural language opinions and will more efficiently bridge the gap between (unstructured) multimodal information and machine-processable data.

Blending scientific theories of emotion with the practical engineering goals of analyzing sentiments in natural- language text will lead to more bio- inspired approaches to the design of intelligent opinion-mining systems capable of handling semantic knowledge, making analogies, learning new affective knowledge, and detecting, perceiving, and “feeling” emotions.