Pulse: Mining Customer Opinions from Free Text

Gamon, Michael; Aue, Anthony; Corston-Oliver, Simon; Ringger, Eric K.

doi:10.1007/11552253_12

Cited by 297 publications

(185 citation statements)

References 7 publications

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“…The same problem is studied in [35] considering gradable adjectives. In [28], a semi-supervised learning method is applied, and in [46], the decision is made by simply summing up opinion words in a sentence. [47,48,49] build models to identify some specific types of opinions in reviews.…”

Section: Assumption Of Sentence-level Sentiment Classificationmentioning

confidence: 99%

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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: Assumption Of Sentence-level Sentiment Classificationmentioning

confidence: 99%

“…Due to its tremendous value for practical applications, there has been an explosive growth of both research in academia and applications in the industry. There are now at least [20][21][22][23][24][25][26][27][28][29][30] companies that offer sentiment analysis services in USA alone. This chapter introduces this research field.…”

mentioning

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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“…Gamon et al [9] presented a prototype system, code-named Pulse, for mining topics and sentiment orientation jointly from free text customer feedback. They described the application of the prototype system to a database of car reviews; a simple but effective technique for clustering sentences, the application of a bootstrapping approach to sentiment classification, and a novel user-interface.…”

Section: Related Researchmentioning

confidence: 99%

Mining Customer Feedback Documents

Stoica¹,

Özyirmidokuz²

2015

IJKE

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Abstract-Managing customer feedback data has become a necessity for firms in order for them to gain competitive advantage in the sector. Analyzing customer complaints' data to find useful information that's hidden is an important step in understanding customers. This important, hidden knowledge must be extracted automatically to allow firms to gain a better understanding of the general market and of their own and their competitors' customers. A firm can learn the needs of customers and show how its products and services satisfy these needs by analyzing these documents.The aim of this research is to summarize and extract data from unstructured customer feedback documents which are about ignoring subscriptions to a telecommunication firm in Turkey. The data are transformed to a collection of documents by generating a document for each record. Text processing techniques are applied. Cosine similarity analysis is used to classify documents into relevant categories. Clusters are determined.

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“…There are several tasks involved in text mining: text categorization and text clustering [16,17], concept/entity extraction [18], production of granular taxonomies [19], sentiment analysis [20,21], document summarization [22][23][24], and entity relation modeling [25,26]. The overall goal is, essentially, to turn text into data for analysis, via application of natural language processing (NLP) [27] and analytical methods.…”

Section: Using Text Mining For Classifying Product Clustersmentioning

confidence: 99%

Using Clustering and Text Mining to Create a Reference Price Database

Carvalho¹,

Paiva²,

Rocha³

et al. 2014

L&NLM

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Resumo -Desde 2004, a Controladoria-Geral da União (CGU) tem publicado diversos dados relacionados a gastos do governo brasileiro no Portal da Transparência. Em 2010, a CGU começou a publicar diariamente as despesas diretas do Governo Federal. No entanto, inconsistências que prejudicam a transparência e prestação de contas foram encontradas nessa base de dados. Esse artigo apresenta como a CGU usa técnicas de agrupamento e mineração de texto para recuperar informações essenciais para a melhoria da prestação de contas do governo, incluindo o que foi comprado, o preço pago por item, o preço de referência por produto, etc. Essa análise permitiu que a CGU chegasse a algumas conclusões preliminares que são apresentadas nesse trabalho como forma de ilustrar os resultados da pesquisa. Finalmente, essa informação será eventualmente disponibilizada no Portal da Transparência, pertmitindo que todo cidadão possa ter acesso a informação de quanto o governo está realmente pagando, de uma forma geral, pelos produtos comprados. Dessa forma,é possível melhorar a prestação de contas e a transparência dos gastos públicos não apenas dentro dô ambito da CGU, comoórgão de controle interno, mas também para todos os cidadãos brasileiros, que, no final, pagam a conta.Palavras-chave -preço de referência, agrupamento, mineração de texto, gasto público, prestação de conta, transparência, empenho e compra governamental.Abstract -Since 2004, Brazil's Office of the Comptroller General (CGU) has been publishing several data related to government expenditures in the Transparency Portal. In 2010, CGU started publishing daily every financial statement produced by the Federal Government. Nevertheless, inconsistencies which hinder accountability have been found in this data base. This paper presents how CGU uses clustering and text mining techniques to retrieve essential information for a good accountability, which includes what was bought, the price paid per item, a price reference per product, etc. This analysis has allowed CGU to draw some preliminary conclusions which are presented as a means to illustrate the research results. Finally, this information will eventually be incorporated in the Transparency Portal, allowing every citizen to understand how much the Government is really paying, in general, for products. Thus, improving social control and providing a solid accountability not only to CGU, as an internal control agency, but also to Brazil's citizens who, in the end, are the ones paying the bill.

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Pulse: Mining Customer Opinions from Free Text

Cited by 297 publications

References 7 publications

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

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

Mining Customer Feedback Documents

Using Clustering and Text Mining to Create a Reference Price Database

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