Predicting the semantic orientation of adjectives

Hatzivassiloglou, Vasileios; McKeown, Kathleen

doi:10.3115/976909.979640

Cited by 738 publications

(317 citation statements)

References 19 publications

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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].…”

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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Section: ) Default Classifiermentioning

confidence: 99%

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

Roy¹,

Czarlinska²,

Tarachandani³

2015

IJKE

View full text Add to dashboard Cite

show abstract

“…One of the key ideas is the one proposed by Hazivassiloglou and McKeown [34]. The technique starts with a list of seed opinion adjective words, and uses them and a set of linguistic constraints or conventions on connectives to identify additional adjective opinion words and their orientations.…”

Section: Corpus-based Approach and Sentiment Consistencymentioning

confidence: 99%

“…For example, sentences (1), (2), (6) and (8) do not express any opinions. The issue of subjectivity has been extensively studied in the literature [34,35,79,80,97,99,100,102,103,104].…”

Section: Objective Of Mining Direct Opinionsmentioning

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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“…Notice that AUC is for binary class problems only thus we report the Kappa statistics instead, which intuitively measures the incremental performance of a classifier with respect to the baseline accuracy 7 . The baseline accuracy is the accuracy of randomly guessing the sentiment class, and in the three class problem the expected accuracy is 33.33%.…”

Section: Inputmentioning

confidence: 99%

“…A related problem is that of studying the semantic orientation, or polarity, of words as defined by Osgood et al [6]. Hatzivassiloglou and McKeown [7] built a log-linear model to predict the semantic orientation of conjoined adjectives using the conjunctions between them. Huettner and Subasic [8] hand-crafted a cognitive linguistic model for affection sentiments based on fuzzy logic.…”

Section: Related Work On Sentimentsmentioning

confidence: 99%

Markov Blankets and Meta-heuristics Search: Sentiment Extraction from Unstructured Texts

Airoldi

Bai

Padman³

2006

Advances in Web Mining and Web Usage Analysis

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Abstract. Extracting sentiments from unstructured text has emerged as an important problem in many disciplines. An accurate method would enable us, for example, to mine online opinions from the Internet and learn customers' preferences for economic or marketing research, or for leveraging a strategic advantage. In this paper, we propose a two-stage Bayesian algorithm that is able to capture the dependencies among words, and, at the same time, finds a vocabulary that is efficient for the purpose of extracting sentiments. Experimental results on online movie reviews and online news show that our algorithm is able to select a parsimonious feature set with substantially fewer predictor variables than in the full data set and leads to better predictions about sentiment orientations than several state-of-the-art machine learning methods. Our findings suggest that sentiments are captured by conditional dependence relations among words, rather than by keywords or high-frequency words.

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Predicting the semantic orientation of adjectives

Cited by 738 publications

References 19 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

Markov Blankets and Meta-heuristics Search: Sentiment Extraction from Unstructured Texts

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