Mining Fine Grained Opinions by Using Probabilistic Models and Domain Knowledge

Miao, Qingliang; Li, Qiudan; Zeng, Daniel

doi:10.1109/wi-iat.2010.193

Cited by 18 publications

(12 citation statements)

References 20 publications

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“…For example, the hotel's features "room," "size," and "cleanness" can be projected onto the aspect "room quality". The typical approaches to feature extraction include statistics based methods, such as one that captures frequently occurring nouns/phrases as feature candidates through association rule mining (Hu and Liu 2004b), a LDA or SVM based method for identifying aspects directly, or a machine learning method based on a lexicalized Hidden Markov Model (LHMMs) (Jin et al 2009) or Conditional Random Fields (CRFs) (Miao et al 2010;Qi and Chen 2010). The opinions associated with features (or aspects) are then identified by looking for nearby adjectives, or through opinion pattern mining (Hu and Liu 2004a;Moghaddam and Ester 2010).…”

Section: Review Elementsmentioning

confidence: 99%

Recommender systems based on user reviews: the state of the art

Chen

Wang

2015

User Model User-Adap Inter

340

187

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In recent years, a variety of review-based recommender systems have been developed, with the goal of incorporating the valuable information in user-generated textual reviews into the user modeling and recommending process. Advanced text analysis and opinion mining techniques enable the extraction of various types of review elements, such as the discussed topics, the multi-faceted nature of opinions, contextual information, comparative opinions, and reviewers' emotions. In this article, we provide a comprehensive overview of how the review elements have been exploited to improve standard content-based recommending, collaborative filtering, and preference-based product ranking techniques. The review-based recommender system's ability to alleviate the well-known rating sparsity and cold-start problems is emphasized. This survey classifies state-of-the-art studies into two principal branches: review-based user profile building and review-based product profile building. In the user profile sub-branch, the reviews are not only used to create term-based profiles, but also to infer or enhance ratings. Multi-faceted opinions can further be exploited to derive the weight/value preferences that users place on particular features. In another sub-branch, the product profile can be enriched with feature opinions or comparative opinions to better reflect its assessment quality. The merit of each branch of work is discussed in terms of both algorithm development and the way in which the proposed

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Section: Review Elementsmentioning

confidence: 99%

Recommender systems based on user reviews: the state of the art

Chen

Wang

2015

User Model User-Adap Inter

340

187

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“…• HIS_RD, DLIREC(U), EliXa(U) and NLANGP(U): HIS_RD was the best result of restaurant dataset in SemEval2014 [29], DLIREC(U) was the best result of laptop dataset in SemEval2014 [11]; EliXa(U) was the best result of restaurant dataset in SemEval2015 [30]; NLANGP(U) was the best result of restaurant dataset in SemEval2016 [31]. U means unconstrained and using additional resources without any constraint, such as lexicons or additional training data.…”

Section: Comparison With Other Methods On F1mentioning

confidence: 99%

“…Jakob et al, in 2010, proposed a model that used CRF to extract opinion entities from data sets in the single domains and across domains [10]. Miao et al used a probability model to reinforce the effect of CRF on the basis of domain information [11]. The DLIREC system won the best grade of aspect term extraction in competition, which used word embedding clustering as the position features of CRF.…”

Section: Introductionmentioning

confidence: 99%

Aspect Term Extraction Based on MFE-CRF

Xiang

Zheng

2018

Information

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This paper is focused on aspect term extraction in aspect-based sentiment analysis (ABSA), which is one of the hot spots in natural language processing (NLP). This paper proposes MFE-CRF that introduces Multi-Feature Embedding (MFE) clustering based on the Conditional Random Field (CRF) model to improve the effect of aspect term extraction in ABSA. First, Multi-Feature Embedding (MFE) is proposed to improve the text representation and capture more semantic information from text. Then the authors use kmeans++ algorithm to obtain MFE and word clustering to enrich the position features of CRF. Finally, the clustering classes of MFE and word embedding are set as the additional position features to train the model of CRF for aspect term extraction. The experiments on SemEval datasets validate the effectiveness of this model. The results of different models indicate that MFE-CRF can greatly improve the Recall rate of CRF model. Additionally, the Precision rate also is increased obviously when the semantics of text is complex.

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“…This resource is improved in [70], where it is enriched with affective information by fusing it with WordNetAffect [71], another semantic resource, to add emotion labels such as Anger, Disgust, Joy and Surprise. In [72], the author presents a new method to classify opinions by combining ontologies with lexical and syntactic knowledge. The work in [73] describes the steps in creating what the authors call a "Human Emotion Ontology" (HEO) which encompasses the domain of human emotions, and shows how this resource can be used to manage affective information related to data issued by online social interaction.…”

Section: Different Approachesmentioning

confidence: 99%