Mining open answers in questionnaire data

Yamanishi, Kenji; Li, Hang

doi:10.1109/mis.2002.1039833

Cited by 69 publications

(37 citation statements)

References 11 publications

(8 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are also studies that focus on building lexicons to facilitate sentiment analysis, such as those reported in [9,18]. Further, sentiment analysis has been applied to various text genres, including open answers in questionnaires [48], newsgroup articles [6], user-generated reviews [7,14,31,32,42] and blog posts (e.g. [5,28]).…”

Section: Background and Related Workmentioning

confidence: 99%

“…Specifically, sentiment analysis is concerned with the automatic identification, extraction, and classification of opinions in texts. It can be used to develop applications that assist decision makers and information analysts in tracking user opinions about topics that they are interested in [14,47,48]. Examples of sentiment analysis include the classification of a movie review as "thumbs up" or "thumbs down" [32], and the classification of a Congressional floor-debate as "support" or "oppose" regarding a proposed legislation [41].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A probabilistic rating inference framework for mining user preferences from reviews

et al. 2011

View full text Add to dashboard Cite

We propose a novel Probabilistic Rating infErence Framework, known as Pref, for mining user preferences from reviews and then mapping such preferences onto numerical rating scales. Pref applies existing linguistic processing techniques to extract opinion words and product features from reviews. It then estimates the sentimental orientations (SO) and strength of the opinion words using our proposed relative-frequency-based method. This method allows semantically similar words to have different SO, thereby addresses a major limitation of existing methods. Pref takes the intuitive relationships between class labels, which are scalar ratings, into consideration when assigning ratings to reviews. Empirical results validated the effectiveness of Pref against several related algorithms, and suggest that Pref can This work is an extension of the preliminary work reported in the papers "Integrating collaborative filtering and sentiment analysis: A rating inference approach" [21] and "Evaluation of a Rating Inference Approach to Utilizing Textual Reviews for Collaborative Recommendation" [22].This work was done when Cane Wing-ki Leung was with the 188 World Wide Web (2011) 14:187-215 produce reasonably good results using a small training corpus. We also describe a useful application of Pref as a rating inference framework. Rating inference transforms user preferences described as natural language texts into numerical rating scales. This allows Collaborative Filtering (CF) algorithms, which operate mostly on databases of scalar ratings, to utilize textual reviews as an additional source of user preferences. We integrated Pref with a classical CF algorithm, and empirically demonstrated the advantages of using rating inference to augment ratings for CF.

show abstract

Section: Background and Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A probabilistic rating inference framework for mining user preferences from reviews

et al. 2011

View full text Add to dashboard Cite

show abstract

“…The mining of the free text component of questionnaires is more challenging and requires recourse to text mining techniques. An example can be found in [21] where two statistical learning techniques are used (rule analysis and correspondence analysis). The mining of complex (multi-media) data remains a focus for current research.…”

Section: Related Workmentioning

confidence: 99%

An Investigation Concerning the Generation of Text Summarisation Classifiers Using Secondary Data

García-Constantino

Coenen

Noble

et al. 2011

Machine Learning and Data Mining in Pattern Recognition

View full text Add to dashboard Cite

Abstract. An investigation into the potential effectiveness of generating text classifiers from secondary data for the purpose of text summarisation is described. The application scenario assumes a questionnaire corpus where we wish to provide a summary regarding the nature of the free text element of such questionnaires, but no suitable training data is available. The advocated approach is to build the desired text summarisation classifiers using secondary data and then apply these classifiers, for the purpose of text summarisation, to the primary data. We refer to this approach using the acronym CGUSD (Classifier Generation Using Secondary Data). The approach is evaluated using a real questionnaire data obtained as part of the SAVSNET project.

show abstract

“…However, it is important to mention that this technique lacks precision and also is easily open to misinterpretation. provided by open questions, however, is higher than those in closed-questions [18]. Moreover, a closed-question provides less information but its results can be more easily analyzed and are obtained faster than with the open one.…”

Section: Data Collection Instruments: a Brief Reviewmentioning

confidence: 93%

Use of Questionnaire-Based Appraisal to Improve the Software Acquisition Process in Small and Medium Enterprises

García

Pacheco

Sumano

Software Engineering Research, Management and Applications

View full text Add to dashboard Cite

Summary. This paper aims to show the application of a "Maturity Questionnaire" in a disciplined way. A Maturity Questionnaire typically is based on the Software Engineering Institute (SEI) published questionnaire; it represents a rigorous technique to collect data in a facilitated manner. The proposed questionnaire focuses in Supplier Agreement Management Process Area of the Capability Maturity Model Integration for Development v1.2. The objective is to obtain a snapshot of the Supplier Agreement Management Process (as a part of a process improvement program), to get a quick judgment of the capability level, and/or a precursor to a full assessment. It is expected that the application of the questionnaire will help to identify those practices that are performed but not documented, which practices need more attention and which are not implemented due to bad management or unawareness.

show abstract

Mining open answers in questionnaire data

Cited by 69 publications

References 11 publications

A probabilistic rating inference framework for mining user preferences from reviews

A probabilistic rating inference framework for mining user preferences from reviews

An Investigation Concerning the Generation of Text Summarisation Classifiers Using Secondary Data

Use of Questionnaire-Based Appraisal to Improve the Software Acquisition Process in Small and Medium Enterprises

Contact Info

Product

Resources

About