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SYNONYMS Sentiment analysis DEFINITION Given a set of evaluative text documents D that contain opinions (or sentiments) about an object, opinion mining aims to extract attributes and components of the object that have been commented on in each document d ∈ D and to determine whether the comments are positive, negative or neutral.39911
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HISTORICAL BACKGROUND
Textual information in the world can be broadly classified into two main categories, facts and opinions. Facts are objective statements about entities and events in the world. Opinions are subjective statements that reflect people’s sentiments or perceptions about the entities and events. Much of the existing research on text information processing has been (almost exclusively) focused on mining and retrieval of factual information, e.g., information retrieval, Web search, and many other text mining and natural language processing tasks. Little work has been done on the processing of opinions until only recently. Yet, opinions are so important that whenever one needs to make a decision one wants to hear others’ opinions. This is not only true for inpiduals but also true for organizations.
One of the main reasons for the lack of study on opinions is that there was little opinionated text before the World Wide Web. Before the Web, when an inpidual needs to make a decision, he/she typically asks for opinions from friends and families. When an organization needs to find opinions of the general public about its products and services, it conducts surveys and focused groups. With the Web, especially with the explosive growth of the user generated content on the Web, the world has changed. One can post reviews of products at merchant sites and express views on almost anything in Internet forums, discussion groups, and blogs, which are collectively called the user generated content. Now if one wants to buy a product, it is no longer necessary to ask one’s friends and families because there are plentiful of product reviews on the Web which give the opinions of the existing users of the product. For a company, it may no longer need to conduct surveys, to organize focused groups or to employ external consultants in order to find consumer opinions or sentiments about its products and those of its competitors.
Finding opinion sources and monitoring them on the Web, however, can still be a formidable task because a large number of perse sources exist on the Web and each source also contains a huge volume of information. In many cases, opinions are hidden in long forum posts and blogs. It is very difficult for a human reader to find relevant sources, extract pertinent sentences, read them, summarize them and organize them into usable forms. An automated opinion mining and summarization system is thus needed. Opinion mining, also known as sentiment analysis, grows out of this need. This article introduces this research area. In particular, it discusses the following topics: (1) the abstract model of opinion mining, (2) sentiment classification, (3) feature-based opinion mining and summarization, and (4) opinion mining from comparative sentences.
Research on opinion mining started with identifying opinion (or sentiment) bearing words, e.g., great, amazing, wonderful, bad, and poor. Many researchers have worked on mining such words and identifying their semantic orientations (i.e., positive or negative). In [5], the authors identified several linguistic rules that can be exploited to identify opinion words and their orientations from a large corpus. This method has been applied, extended and improved in [3, 8, 12]. In [6, 9], a bootstrapping approach is proposed, which uses a small set of given seed opinion words to find their synonyms and antonyms in WordNet (http://wordnet.princeton.edu/). The next major development is sentiment classification of product reviews at the document level [2, 11, 13]. The objective of this task is to classify each review document as expressing a positive or a negative sentiment about an object (e.g., a movie, a camera, or a car). Several researchers also studied sentence-level sentiment classification [9, 14, 15], i.e., classifying each sentence as expressing a positive or a negative opinion. The model of feature-based opinion mining and summarization is proposed in [6, 10]. This model gives a more complete formulation of the opinion mining problem. It identifies the key pieces of information that should be mined and describes how a structured opinion summary can be produced from unstructured texts. The problem of mining opinions from comparative sentences is introduced in [4, 7].