Mining product reputations on the Web

Morinaga, Soichiro; Yamanishi, Kenji; Tateishi, Kenji; Fukushima, Toshikazu

doi:10.1145/775094.775098

Cited by 145 publications

(98 citation 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%

“…Examples of such applications are recognizing inflammatory messages (Spertus, 1997), tracking sentiment timelines in on-line discussions (Tong, 2001), extracting investor sentiment from stock message boards (Das and Chen, 2001), distinguishing editorials from news articles (e.g., (Wiebe, Wilson, and Bell, 2001;Yu and Hatzivassiloglou, 2003)), review classification (e.g., (Turney, 2002;Pang, Lee, and Vaithyanathan, 2002;Morinaga et al, 2002;Dave, Lawrence, and Pennock, 2003;Nasukawa and Yi, 2003;Beineke, Hastie, and Vaithyanathan, 2004;Mullen and Collier, 2004;Kudo and Matsumoto, 2004;Pang and Lee, 2005;Whitelaw, Garg, and Argamon, 2005)), mining opinions from product reviews (e.g., (Morinaga et al, 2002;Nasukawa and Yi, 2003;Yi et al, 2003;Hu and Liu, 2004;Popescu and Etzioni, 2005)), automatic expressive text-tospeech synthesis (Alm, Roth, and Sproat, 2005), information extraction (e.g., (Riloff, Wiebe, and Phillips, 2005)), and question answering (e.g., (Yu and Hatzivassiloglou, 2003;Stoyanov, Cardie, and Wiebe, 2005)). …”

Section: Research In Subjectivity and Sentiment Analysismentioning

confidence: 99%

“…Other researchers have worked to identify opinions below the sentence level (Kim and Hovy, 2004;Morinaga et al, 2002;Dave, Lawrence, and Pennock, 2003;Nasukawa and Yi, 2003;Yi et al, 2003;Hu and Liu, 2004;Popescu and Etzioni, 2005). Kim and Hovy (2004) identify sentences that mention particular topics, use a named entity tagger to identify the closest entity in the text, and then use the topic and entity phrases to define regions that are used for classifiying sentiments.…”

Section: Related Workmentioning

confidence: 99%

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Recognizing Strong and Weak Opinion Clauses

Wilson

Wiebe

Hwa

2006

Computational Intelligence

256

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%

Section: Research In Subjectivity and Sentiment Analysismentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Recognizing Strong and Weak Opinion Clauses

Wilson

Wiebe

Hwa

2006

Computational Intelligence

256

225

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“…Manual approach is very timeconsuming [15,65,94,106] and thus it is not usually used alone, but combined with automated approaches as the final check because automated methods make mistakes. Below, we discuss the two automated approaches.…”

Section: Opinion Lexicon Generationmentioning

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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“…in the literature. The research in this field has started in the early 2000s with the works of [1], [1], [3], [4], [5] and [6]. The use of the term sentiment analysis first appeared in [7].…”

Section: Introductionmentioning

confidence: 99%

Sentiment analysis using a random forest classifier on turkish web comments

Pervan¹,

Keleş²

2017

Communications Faculty of Science University of Ankara

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Abstract. Sentiment analysis is an active research area since early 2000s as a field of text classification. Most of the studies in this field focus on the analysis using the text in English language, where the Turkish and the other languages have fallen behind. The purpose of this research is to contribute to the text analysis in Turkish language using the contents that we access through web sites. In particular, we deduce the sentiment behind noisy product reviews and comments in a highly popular commercial web page. In this context, we generate a unique dataset that includes 9100 product review samples for training our classification model. There are different word representation methods that are utilized in sentiment analysis, such as bag-ofwords and n-gram models. In this work, we generated our word models using the word2vec algorithm. In this model, each word in the vocabulary is represented as a vector of 300 dimensions. We utilize 70% of our dataset in the training of a Random Forest Model and make binary classification of sentiments as being positive or negative, utilizing the ratings of the user for the product as classification labels. In the highly noisy and unfiltered comments, we achieve an accuracy of 84.23%.

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Mining product reputations on the Web

Cited by 145 publications

References 0 publications

Recognizing Strong and Weak Opinion Clauses

Recognizing Strong and Weak Opinion Clauses

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

Sentiment analysis using a random forest classifier on turkish web comments

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