Determining the sentiment of opinions

Kim, Soo-Min; Hovy, Eduard

doi:10.3115/1220355.1220555

Cited by 981 publications

(484 citation statements)

References 13 publications

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“…As discussed in Section II, the sentiment related to a particular topic / entity is dictated by opinion bearing terms present in the text surrounding the topic [3]. In [11] we also see that in terms of accuracy, a region of text works better than just the sentence containing the topic, for sentiment prediction. Hence, the preprocessor extracts text segments called "snippets", surrounding the topic terms specified by the user, from the documents returned by the Document Retrieval Engine.…”

Section: B Sentiment Enginementioning

confidence: 67%

“…Bing Liu shows in his work that product reviews contain different sentiments associated with different product features [8]- [10]. Hovy and Kim"s work involves extracting opinion bearing words from a predefined sentiment region around a specified topic and then aggregating the score for the individual words to compute the overall sentiment related to the topic [11]. Nasukawa et al also use similar approach to extract sentiment scores for a given topic and then use NLP techniques to associate the extracted sentiment to the topic [12].We draw on these approaches to extract sentiment scores for the user-specified topic of interest.…”

Section: A Sentiment Classification Techniquesmentioning

confidence: 99%

“…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%

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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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Section: B Sentiment Enginementioning

confidence: 67%

Section: A Sentiment Classification Techniquesmentioning

confidence: 99%

Section: ) Default Classifiermentioning

confidence: 99%

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

Roy¹,

Czarlinska²,

Tarachandani³

2015

IJKE

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“…In the case of product reviews and blogs, opinion holders are usually the authors of the posts. Opinion holders are more important in news articles because they often explicitly state the person or organization that holds a particular opinion [5,14,46]. For example, the opinion holder in the sentence "John expressed his disagreement on the treaty" is "John".…”

Section: Definition (Opinion Holder)mentioning

confidence: 99%

“…However, when the objects are events and topics, the term feature may not sound natural. Indeed in some other domains, researchers also use the term topic [46] or aspect [50,84] to mean feature. In this chapter, we choose to use the term feature along with the term object.…”

Section: Examplementioning

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...

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The Economic Impact of User‐Generated and Firm‐Generated Online Content: Directions for Advancing the Frontiers in Electronic Commerce Research

Ghose

2008

Statistical Methods in E‐Commerce Research

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Determining the sentiment of opinions

Cited by 981 publications

References 13 publications

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

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

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

The Economic Impact of User‐Generated and Firm‐Generated Online Content: Directions for Advancing the Frontiers in Electronic Commerce Research

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