Extracting product features and opinions from reviews

Popescu, Ana-Maria; Etzioni, Oren

doi:10.3115/1220575.1220618

Cited by 1,145 publications

(731 citation statements)

References 26 publications

(24 reference statements)

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“…This includes identifying expressions or sentences that are subjective in the context of a particular text or conversation (e.g., Yu and Hatzivassiloglou, 2003;Nasukawa and Yi, 2003;Popescu and Etzioni, 2005)), identifying particular types of attitudes (e.g., (Gordon et al, 2003;Liu, Lieberman, and Selker, 2003)), recognizing the polarity or sentiment of phrases or sentences (e.g., (Morinaga et al, 2002;Yu and Hatzivassiloglou, 2003;Nasukawa and Yi, 2003;Yi et al, 2003;Kim and Hovy, 2004;Hu and Liu, 2004;Popescu and Etzioni, 2005;Wilson, Wiebe, and Hoffman, 2005)), identifying who is expressing an opinion (Kim and Hovy, 2004;Choi et al, 2005), and identifying levels of attributions (e.g., that it is according to China that the U.S. believes something) (Breck and Cardie, 2004).…”

Section: Research In Subjectivity and Sentiment Analysismentioning

confidence: 99%

Recognizing Strong and Weak Opinion Clauses

Wilson

Wiebe

Hwa

2006

Computational Intelligence

255

225

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There has been a recent swell of interest in the automatic identification and extraction of opinions and emotions in text. In this paper, we present the first experimental results classifying the intensity of opinions and other types of subjectivity and classifying the subjectivity of deeply nested clauses. We use a wide range of features, including new syntactic features developed for opinion recognition. We vary the learning algorithm and the feature organization to explore the effect this has on the classification task. In 10-fold cross-validation experiments using support vector regression, we achieve improvements in mean-squared error over baseline ranging from 49% to 51%. Using boosting, we achieve improvements in accuracy ranging from 23% to 96%.

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Section: Research In Subjectivity and Sentiment Analysismentioning

confidence: 99%

Recognizing Strong and Weak Opinion Clauses

Wilson

Wiebe

Hwa

2006

Computational Intelligence

255

225

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“…Previous work on unsupervised sentiment classification has shown that adjectives and adverbs are good indicators of sentiment (Hatzivassiloglou, 1997(Hatzivassiloglou, , 2000, [19], Turney 2002 [5]). It has also been shown that adjectives present around a given topic are indicative of sentiment related to the particular topic [11], [20]. Hence we first use a Part of Speech (POS) tagger to identify adjectives present in the snippet.…”

Section: ) Default Classifiermentioning

confidence: 99%

Framework for Sentiment Aware Queries and Results in Search Using Oracle Text

Roy¹,

Czarlinska²,

Tarachandani³

2015

IJKE

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Abstract-Users today need to express their informational need in a way such that the search results can be further analyzed to directly address the need, instead of merely returning a list of lexical hits. In this paper we address the problem of extracting sentiment metadata related to the user's topic or entity of interest along with the search results. We propose a framework that provides the flexibility of specifying a sentiment related informational need associated with a particular topic of interest, along with the standard keyword query. This information is then used to extract sentiment scores for the expressed entity of interest based on the "hitlist" of most relevant documents returned by the main search query. Our experimental results based on product and movie review datasets, demonstrate the advantages of embedding the sentiment processing within the search engine framework.Index Terms-Sentiment analysis, information retrieval, query framework, text analytics, sentiment aware search.

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“…The precision of step 1 of the above algorithm was improved by Popescu and Etzioni in [77]. Their algorithm tries to remove those noun phrases that may not be product features.…”

Section: Feature Extraction From Reviews Of Formatmentioning

confidence: 99%

“…Here, we only describe a lexicon-based approach to solving the problem [19,36]. See a more complex method based on relaxation labeling in [77].…”

Section: Opinion Orientation Identificationmentioning

confidence: 99%

The Customer is Always Right: Sentiment Analysis for Bank Service Quality

Brunova¹,

Bidulya²

2017

TSU Herald. Hum Res. Humanitates

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Textual information in the world can be broadly categorized into two main types: facts and opinions. Facts are objective expressions about entities, events and their properties. Opinions are usually subjective expressions that describe people's sentiments, appraisals or feelings toward entities, events and their properties. The concept of opinion is very broad. In this chapter, we only focus on opinion expressions that convey people's positive or negative sentiments. Much of the existing research on textual information processing has been focused on mining and retrieval of factual information, e.g., information retrieval, Web search, text classification, text clustering and many other text mining and natural language processing tasks. Little work had been done on the processing of opinions until only recently. Yet, opinions are so important that whenever we need to make a decision we want to hear others' opinions. This is not only true for individuals but also true for organizations.One of the main reasons for the lack of study on opinions is the fact that there was little opinionated text available before the World Wide Web. Before the Web, when an individual needed to make a decision, he/she typically asked for opinions from friends and families. When an organization wanted to find the opinions or sentiments of the general public about its products and services, it conducted opinion polls, surveys, and focus groups. However, with the Web, especially with the explosive growth of the usergenerated content on the Web in the past few years, the world has been transformed.The Web has dramatically changed the way that people express their views and opinions. They can now post reviews of products at merchant sites and express their views on almost anything in Internet forums, discussion groups, and blogs, which are collectively called the user-generated content. This online wordof-mouth behavior represents new and measurable sources of information with many practical applications. Now if one wants to buy a product, he/she is no longer limited to asking his/her friends and families because there are many product reviews on the Web which give opinions of existing users of the product. For a company, it may no longer be necessary to conduct surveys, organize focus groups or employ external consultants in order to find consumer opinions about its products and those of its competitors because the user-generated content on the Web can already give them such information.However, finding opinion sources and monitoring them on the Web can still be a formidable task because there are a large number of diverse sources, and each source may also have a huge volume of opinionated text (text with opinions or sentiments). In many cases, opinions are hidden in long forum posts and blogs. It is difficult for a human reader to find relevant sources, extract related sentences with opinions, read them, summarize them, and organize them into usable forms. Thus, automated opinion discovery and summarization systems are needed. Sentiment an...

show abstract

Extracting product features and opinions from reviews

Cited by 1,145 publications

References 26 publications

Recognizing Strong and Weak Opinion Clauses

Recognizing Strong and Weak Opinion Clauses

Framework for Sentiment Aware Queries and Results in Search Using Oracle Text

The Customer is Always Right: Sentiment Analysis for Bank Service Quality

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