Hidden sentiment association in chinese web opinion mining

Su, Qi; Xu, Xinying; Guo, Honglei; Guo, Zhiyong; Wang, Xian; Zhang, Xiaoxun; Swen, Bin; Su, Zhong

doi:10.1145/1367497.1367627

Cited by 198 publications

(79 citation statements)

References 16 publications

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“…Other related works on feature extraction mainly use the ideas of topic modeling and clustering to capture topics/features in reviews [58,62,89,93,106] After the extraction of object features, two additional problems need to be solved:…”

Section: Feature Extraction From Reviews Of Formatmentioning

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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Section: Feature Extraction From Reviews Of Formatmentioning

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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“…Both LRTBOOT and CAFE are used to provide the input features to them. We set α = 0.6 for MuReinf, because their study (Su et al, 2008) showed that the method achieved best results at α > 0.5. All three methods utilize dictionary-based semantic similarity to some extent.…”

Section: Evaluations On Aspect Discoverymentioning

confidence: 99%

Clustering for Simultaneous Extraction of Aspects and Features from Reviews

Chen

Martineau

Cheng

et al. 2016

Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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This paper presents a clustering approach that simultaneously identifies product features and groups them into aspect categories from online reviews. Unlike prior approaches that first extract features and then group them into categories, the proposed approach combines feature and aspect discovery instead of chaining them. In addition, prior work on feature extraction tends to require seed terms and focus on identifying explicit features, while the proposed approach extracts both explicit and implicit features, and does not require seed terms. We evaluate this approach on reviews from three domains. The results show that it outperforms several state-of-the-art methods on both tasks across all three domains.

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“…ARM has limitation for the task of feature identification, as it depends on frequency of item sets i.e., frequent but invalid features are not extracted correctly and rare but valid features maybe neglected. To address the problem of feature-based opinion mining, A mutual reinforcement clustering (MRC) approach is introduced by Suet al [26] to mine the associations between product feature categories and opinion word groups. MRC technique use multisource knowledge including semantic and textual structure.…”

Section: Literature Surveymentioning

confidence: 99%

A Survey Paper on Extraction of Web Services for Effective User Review Sentiment Analysis

2016

IJSR

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This paper presents a survey on features Extraction of Web Services for effective User Review Sentiment Analysis.Recognition of opinion features from online user reviews is a task to find on which feature user is going to put his opinion. For opinion feature identification there are number of existing techniques but, features are extracted from single corpus. Quality of Service has become a standard way of evaluating web services and selecting the one that suites user interests the best. Traditional methods adopt a fixed set of QoS parameters and typical ones include response time, fee, and availability. There currently lacks an effective way of identifying quality features that users are actually interested in when choosing a service. Meanwhile, the traditional way of collecting QoS values relies on either public information released by service providers or test results from repeatedly invoking a service. The last section of paper will discuss the disadvantages of current systems and will state the future scope.

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Hidden sentiment association in chinese web opinion mining

Cited by 198 publications

References 16 publications

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

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

Clustering for Simultaneous Extraction of Aspects and Features from Reviews

A Survey Paper on Extraction of Web Services for Effective User Review Sentiment Analysis

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