Product feature categorization with multilevel latent semantic association

Guo, Honglei; Zhu, Hengyue; Guo, Zhiyong; Zhang, XiaoXun; Su, Zhong

doi:10.1145/1645953.1646091

Cited by 121 publications

(53 citation statements)

References 15 publications

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“…Guo et al [29] proposed an unsupervised product-feature categorization technique with multilevel latent semantic association. Using unlabeled review corpus and the list of product features, first LaSA model capture latent semantic association among words of product features.…”

Section: Unsupervised Approachesmentioning

confidence: 99%

“…After that, second LaSA is used to categorize the product features on the basis of their latent topic structure. The benefit of this technique is that it is language and domain independent [29]. Unlike unsupervised, supervised technique works better for feature categorization but it requires a lot of human efforts for labeling training data.…”

Section: Unsupervised Approachesmentioning

confidence: 99%

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Literature Review on Feature Identification in Sentiment Analysis

Hussain¹,

Sattar²,

Afzal³

2015

IJCA

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Now-a-days the volume of opinions about products, issues, events, and politics etc. on different social, e-commerce and review sites grows very rapidly. From both opinion holder and opinion target point of view, it is very difficult and time consuming task to analyze all the reviews from this massive amount of data on the web. So, there is a need of efficient method that automatically extracts the opinions and relevant features of the opinion target from the reviews and finally generates the feature wise summary. Sometimes people may use different words to express same feature, this may produce a misperception in the results during feature wise summary generation. To avoid this, we need to categorize similar features for precise classification of opinions based on these feature groups. Therefore, our study is targeting the most important tasks of feature based sentiment analysis that are feature extraction and feature categorization. This paper is about to cover the currently available techniques in these two areas. We have also focused on least addressed area in this domain giving an opportunity to researchers for future work. General TermsPattern Recognition

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Section: Unsupervised Approachesmentioning

confidence: 99%

Section: Unsupervised Approachesmentioning

confidence: 99%

Literature Review on Feature Identification in Sentiment Analysis

Hussain¹,

Sattar²,

Afzal³

2015

IJCA

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“…Two important observations were made, which allowed us to extract must-link and cannot-link constraints automatically. Experiments show that the proposed constrained-LDA produces significantly better results than the original LDA and the latest mLSA [16] which also uses LDA.…”

Section: Introductionmentioning

confidence: 98%

“…In [16], product features were grouped using a multilevel latent semantic association technique, called mLSA. At the first level, all the words in product feature terms (each feature term can have more than one word) were grouped into a set of concepts/topics using LDA.…”

Section: Introductionmentioning

confidence: 99%

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Constrained LDA for Grouping Product Features in Opinion Mining

Zhai

Liu

et al. 2011

Advances in Knowledge Discovery and Data Mining

105

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Abstract. In opinion mining of product reviews, one often wants to produce a summary of opinions based on product features/attributes. However, for the same feature, people can express it with different words and phrases. To produce an effective summary, these words and phrases, which are domain synonyms, need to be grouped under the same feature. Topic modeling is a suitable method for the task. However, instead of simply letting topic modeling find groupings freely, we believe it is possible to do better by giving it some pre-existing knowledge in the form of automatically extracted constraints. In this paper, we first extend a popular topic modeling method, called LDA, with the ability to process large scale constraints. Then, two novel methods are proposed to extract two types of constraints automatically. Finally, the resulting constrained-LDA and the extracted constraints are applied to group product features. Experiments show that constrained-LDA outperforms the original LDA and the latest mLSA by a large margin.

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Sentiment Analysis on Social Media for Emotional Prediction During COVID‐19 Pandemic Using Efficient Machine Learning Approach

Sivanantham¹

2021

Computational Intelligence and Healthcare Informatics

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Product feature categorization with multilevel latent semantic association

Cited by 121 publications

References 15 publications

Literature Review on Feature Identification in Sentiment Analysis

Literature Review on Feature Identification in Sentiment Analysis

Constrained LDA for Grouping Product Features in Opinion Mining

Sentiment Analysis on Social Media for Emotional Prediction During COVID‐19 Pandemic Using Efficient Machine Learning Approach

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