Sentiment Analysis最新文献

{"title":"Aspect Sentiment Classification","authors":"Bing-Rong Liu","doi":"10.1017/CBO9781139084789.006","DOIUrl":"https://doi.org/10.1017/CBO9781139084789.006","url":null,"abstract":"","PeriodicalId":305421,"journal":{"name":"Sentiment Analysis","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130531819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Problem of Sentiment Analysis","authors":"Bing Liu","doi":"10.1017/CBO9781139084789.003","DOIUrl":"https://doi.org/10.1017/CBO9781139084789.003","url":null,"abstract":"In this chapter, we define an abstraction of the sentiment analysis problem. This abstraction gives us a statement of the problem and enables us to see a rich set of interrelated subproblems. It is often said that if we cannot structure a problem, we probably do not understand the problem. The objective of the definitions is thus to abstract a structure from the complex and intimidating unstructured natural language text. The structure serves as a common framework to unify various existing research directions and enable researchers to design more robust and accurate solution techniques by exploiting the interrelationships of the subproblems. From a practical application point of view, the definitions let practitioners see what subproblems need to be solved in building a sentiment analysis system, how the subproblems are related, and what output should be produced. Unlike factual information, sentiment and opinion have an important characteristic, namely, they are subjective. The subjectivity comes from many sources. First of all, different people may have different experiences and thus different opinions. For example, one person bought a camera of a particular brand and had a very good experience with it. She naturally has a positive opinion or sentiment about the camera. However, another person who also bought a camera of the same brand had some issues with it because he might just be unlucky and got a defective unit. He thus has a negative opinion. Second, different people may see the same thing in different ways because everything has two sides. For example, when the price of a stock is falling, one person may feel very sad because he bought the stock when the price was high, but another person may be very happy because it is an opportunity to short sell the stock to make good profits. Furthermore, different people may have different interests and/or different ideologies.","PeriodicalId":305421,"journal":{"name":"Sentiment Analysis","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124719524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sentence Subjectivity and Sentiment Classification","authors":"Bing Liu","doi":"10.1017/CBO9781139084789.005","DOIUrl":"https://doi.org/10.1017/CBO9781139084789.005","url":null,"abstract":"As discussed in the previous chapter, document-level sentiment classification is too coarse for practical applications. We now move to the sentence level and look at methods that classify sentiment expressed in each sentence. The goal is to classify each sentence in an opinion document (e.g., a product review) as expressing a positive, negative, or neutral opinion. This gets us closer to real-life sentiment analysis applications, which require opinions on sentiment targets. Sentence-level classification is about the same as document-level classification because sentences can be regarded as short documents. Sentence-level classification, however, is often harder because the information contained in a typical sentence is much less than that contained in a typical document because of their length difference. Most document-level sentiment classification research papers ignore the neutral class mainly because it is more difficult to perform three-class classification (positive, neutral, and negative) accurately. However, for sentence-level classification, the neutral class cannot be ignored because an opinion document can contain many sentences that express no opinion or sentiment. Note that neutral opinion often means no opinion or sentiment expressed. One implicit assumption that researchers make about sentence-level classification is that a sentence expresses a single sentiment. Let us start our discussion with an example review: I bought a Lenovo Ultrabook T431s two weeks ago. It is really light, quiet and cool. The new touchpad is great too. It is the best laptop that I have ever had although it is a bit expensive . The first sentence expresses no sentiment or opinion as it simply states a fact. It is thus neutral. All other sentences express some sentiment. Sentence-level sentiment classification is defined as follows: Definition 4.1 (Sentence sentiment classification): Given a sentence x , determine whether x expresses a positive, negative, or neutral (or no) opinion. As we can see, like document-level sentiment classification, sentence-level sentiment classification also does not consider opinion or sentiment targets. However, in most cases, if the system is given a set of entities and their aspects, the sentiment about them in a sentence can just take the sentiment of the sentence.","PeriodicalId":305421,"journal":{"name":"Sentiment Analysis","volume":"33 5 Pt B 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123004612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of Comparative Opinions","authors":"Bing Liu","doi":"10.1017/CBO9781139084789.009","DOIUrl":"https://doi.org/10.1017/CBO9781139084789.009","url":null,"abstract":"Apart from directly expressing positive or negative opinions about an entity and/or its aspects, one can also express opinions by comparing similar entities. Such opinions are called comparative opinions (Jindal and Liu, 2006a, 2006b). Comparative opinions have different semantic meanings than regular opinions and also different syntactic forms. For example, a typical regular opinion sentence is “ The voice quality of this phone is amazing ,” and a typical comparative opinion sentence is “ The voice quality of Moto X is better than that of iPhone 5 .” This comparative sentence does not say that any phone's voice quality is good or bad, but simply states a relative ordering in terms of voice quality of the two smart phones. Like regular sentences, comparative sentences can be opinionated or not-opinionated. The above comparative sentence is clearly opinionated because it explicitly expresses a comparative sentiment, while the sentence “ Samsung Galaxy 4 is larger than iPhone 5 ” expresses no sentiment, at least not explicitly. In this chapter, we first define the problem of comparative opinion mining and then present some existing methods for solving the problem. We will study superlative opinions as well because their semantic meanings and handling methods are similar. Problem Definition A comparative sentence usually expresses a relation based on the similarities or differences of more than one entity. Linguists have studied comparatives in the English language for a long time. Lerner and Pinkal (1992) defined comparatives as universal quantifiers over degrees. For example, in the sentence “ John is taller than he was ,” the degree d is John's height and John is tall to degree d . In other words, comparatives are used to express explicit orderings between objects with respect to the degree or amount to which they possess some gradable property (Kennedy, 2005). Two broad types of comparatives are as follows: Metalinguistic comparatives . Compare the extent to which an entity has one property to a greater or lesser extent than another property, for example, “ Ronaldo is angrier than upset .” […]","PeriodicalId":305421,"journal":{"name":"Sentiment Analysis","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123372445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}