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SCIENTIFIC FUNDAMENTALS
Model of Opinion Mining
In general, opinions can be expressed on anything, e.g., a product, a service, a topic, an inpidual, an organization, or an event. The general term object is used to denote the entity that has been commented on. An object has a set of components (or parts) and a set of attributes. Each component may also have its sub-components and its set of attributes, and so on. Thus, the object can be hierarchically decomposed based on the part-of relationship.
Definition (object): An object O is an entity which can be a product, topic, person, event, or organization. It is associated with a pair, O: (T, A), where T is a hierarchy or taxonomy of components (or parts) and sub-components of O, and A is a set of attributes of O. Each component has its own set of sub-components and attributes.
In this hierarchy or tree, the root is the object itself. Each non-root node is a component or sub-component of the object. Each link is a part-of relationship. Each node is associated with a set of attributes. An opinion can be expressed on any node and any attribute of the node.
However, for an ordinary user, it is probably too complex to use a hierarchical representation. To simplify it, the tree is flattened. The word “features” is used to represent both components and attributes. Using features for objects (especially products) is quite common in practice. Note that in this definition the object itself is also a feature, which is the root of the tree.
Let an evaluative document be d, which can be a product review, a forum post or a blog that evaluates a particular object O. In the most general case, d consists of a sequence of sentences d = 〈s1, s2, …, sm〉.
Definition (opinion passage on a feature): The opinion passage on a feature f of the object O evaluated in d is a group of consecutive sentences in d that expresses a positive or negative opinion on f. This means that it is possible that a sequence of sentences (at least one) together expresses an opinion on an object or a feature of the object. It is also possible that a single sentence expresses opinions on more than one feature, e.g., “The picture quality of this camera is good, but the battery life is short”.
Definition (opinion holder): The holder of a particular opinion is a person or an organization that holds the opinion. In the case of product reviews, forum postings and blogs, opinion holders are usually the authors of the posts. Opinion holders are important in news articles because they often explicitly state the person or organization that holds a particular opinion [9]. For example, the opinion holder in the sentence “John expressed his disagreement on the treaty” is “John”.
Digital_camera_1:
CAMERA:
Positive: 125 <inpidual review sentences>
Negative: 7 <inpidual review sentences>
Feature: picture quality
Positive: 123 <inpidual review sentences>
Negative: 6 <inpidual review sentences>
Feature: size
Positive: 82 <inpidual review sentences>
Negative: 10 <inpidual review sentences>
…
Fig. 1. An example of a feature-based summary of opinions
Definition (semantic orientation of an opinion): The semantic orientation of an opinion on a feature f states whether the opinion is positive, negative or neutral. Putting things together, a model for an object and a set of opinions on the features of the object can be defined, which is called the feature-based opinion mining model.
Model of Feature-Based Opinion Mining: An object O is represented with a finite set of features, F ={f1, f2, …, fn}, which includes the object itself. Each feature fi ∈ F can be expressed with a finite set of words or phrases Wi, which are synonyms. That is, there is a set of corresponding synonym sets W = {W1, W2, …, Wn} for the n features. In an evaluative document d which evaluates object O, an opinion holder j comments on a subset of the features Sj ⊆ F. For each feature fk ∈ Sj that opinion holder j comments on, he/she chooses a word or phrase from Wk to describe the feature, and then expresses a positive, negative or neutral opinion on fk. The opinion mining task is to discover all these hidden pieces of information from a given evaluative document d.