Learning extraction patterns for subjective expressions

Riloff, Ellen; Wiebe, Janyce

doi:10.3115/1119355.1119369

Cited by 694 publications

(463 citation statements)

References 21 publications

(15 reference statements)

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“…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%

“…For example, one of the early works reported by Wiebe et al [98] performed subjectivity classification using the naïve Bayesian classifier. Subsequent research also used other learning algorithms [35,80,103,107].…”

Section: Sentence-level Subjectivity and Sentiment Classificationmentioning

confidence: 99%

“…To save the manual labeling effort, a bootstrapping approach to label training data automatically is reported in [80,81]. The algorithm works by first using two high precision classifiers (HP-Subj and HP-Obj) to automatically identify some subjective and objective sentences.…”

Section: Sentence-level Subjectivity and Sentiment Classificationmentioning

confidence: 99%

See 2 more Smart Citations

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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“…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%

Section: Sentence-level Subjectivity and Sentiment Classificationmentioning

confidence: 99%

See 1 more Smart Citation

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

Brunova¹,

Bidulya²

2017

TSU Herald. Hum Res. Humanitates

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show abstract

“…However, merely relying on the aforementioned features may not lead to satisfactory sentiment detection results since sentiment is often context-dependent. Also, people tend to convey sentiment in more subtle linguistic structures or patterns [16]. Such patterns are usually derived from the syntactic [16] or semantic relations [6] between words in text.…”

Section: Introductionmentioning

confidence: 99%

“…Also, people tend to convey sentiment in more subtle linguistic structures or patterns [16]. Such patterns are usually derived from the syntactic [16] or semantic relations [6] between words in text. For example, the adjective word "mean" when preceded by a verb, constitutes a pattern of negative sentiment as in: "she said mean things".…”

Section: Introductionmentioning

confidence: 99%

Semantic Patterns for Sentiment Analysis of Twitter

Saif

Fernández

et al. 2014

Lecture Notes in Computer Science

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Abstract. Most existing approaches to Twitter sentiment analysis assume that sentiment is explicitly expressed through affective words. Nevertheless, sentiment is often implicitly expressed via latent semantic relations, patterns and dependencies among words in tweets. In this paper, we propose a novel approach that automatically captures patterns of words of similar contextual semantics and sentiment in tweets. Unlike previous work on sentiment pattern extraction, our proposed approach does not rely on external and fixed sets of syntactical templates/patterns, nor requires deep analyses of the syntactic structure of sentences in tweets. We evaluate our approach with tweet-and entity-level sentiment analysis tasks by using the extracted semantic patterns as classification features in both tasks. We use 9 Twitter datasets in our evaluation and compare the performance of our patterns against 6 state-of-the-art baselines. Results show that our patterns consistently outperform all other baselines on all datasets by 2.19% at the tweet-level and 7.5% at the entity-level in average F-measure.

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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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Learning extraction patterns for subjective expressions

Cited by 694 publications

References 21 publications

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

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

Semantic Patterns for Sentiment Analysis of Twitter

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

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