Sentiment information extraction of comparative sentences based on CRF model

Wang, Wei; Xin, Guodong; Wang, Bailing; Huang, Junheng; Liu, Yang

doi:10.2298/csis161229031w

Cited by 12 publications

(11 citation statements)

References 16 publications

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“…It is common in English for the prepositional phrase (PP) to come at the conclusion of a sentence, which can easily lead to a syntactic structure misunderstanding over whether the PP modifies the preceding noun or the preceding verb. As a result, the study of prepositional phrase attachment in English places a high value on resolving the problem of PP attachment [1][2][3][4][5]. Many ways have been developed one after another, starting with the original rule-based approach and progressing to statistical methods, unsupervised and supervised learning, and the now common lexical vector representation, among others.…”

Section: Introductionmentioning

confidence: 99%

An Algorithm for Automatic Recognition of Chinese Spatial Prepositional Phrases for Academic Texts

Gao

2022

Wireless Communications and Mobile Computing

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Automatic recognition of prepositional phrases has always been pursued in English translation, and its accurate recognition is crucial for various activities and applications in the field of natural language processing. This paper proposes an automatic recognition algorithm for Chinese spatial prepositional phrases in academic texts, which can not only identify parallel prepositional phrases but also improve the recognition accuracy of nested prepositional phrases. First, the method employs a simple noun phrase recognition model to identify and fuse phrase information contained in the corpus, thereby simplifying the corpus and minimizing the internal complexity of prepositional phrases. Second, the method uses the CRF model to identify nested inner prepositional phrases; if there is nesting, it identifies the inner layer of nesting; if there is no nesting, it identifies prepositional phrases; finally, it fuses the inner prepositions identified in the initial corpus phrase, modifies its feature information, and retrains the foreign preposition phrase recognition model for recognition. After the internal and external prepositional phrases are automatically identified, the double error correction method is used to correct the identified prepositional phrases. The experimental analysis shows that the accuracy rate, recall rate, and F -value of the method for identifying prepositional phrases are 95.33%, 94.32%, and 94.73%, respectively, which are higher than those of the other methods for identifying prepositional phrases, which effectively improves the recognition rate of prepositional phrases.

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Section: Introductionmentioning

confidence: 99%

An Algorithm for Automatic Recognition of Chinese Spatial Prepositional Phrases for Academic Texts

Gao

2022

Wireless Communications and Mobile Computing

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“…Prior studies of fine-grained sentiment analysis usually employ two types of approaches: machine learning based and rule based. Machine learning-based approaches often treat the task as a multi-class classification problem and solve it with statistical language models like CRFs (Zhang et al, 2014;Wang et al, 2017b), neural networks (Poria et al, 2016a), and deep learning models (Chen et al, 2017). Generally, the more complex and powerful statistical models are, the greater amount of training data is needed.…”

Section: Fine-grained Sentiment Analysismentioning

confidence: 99%

“…Fine-grained sentiment analysis of UGC is usually conducted with machine learning-based methods or rule-based methods. The former approaches mostly use statistical language models, like conditional random fields (CRFs) (Zhang et al, 2014;Wang et al, 2017b), neural networks (Poria et al, 2016a), to extract semantic information from a corpus of texts. Rule-based approaches often employ semantic rules and lexicons with expert knowledges.…”

Section: Introductionmentioning

confidence: 99%

Intelligent product redesign strategy with ontology-based fine-grained sentiment analysis

Zhu

Jin

et al. 2021

AIEDAM

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With the increasing demand for a personalized product and rapid market response, many companies expect to explore online user-generated content (UGC) for intelligent customer hearing and product redesign strategy. UGC has the advantages of being more unbiased than traditional interviews, yielding in-time response, and widely accessible with a sheer volume. From online resources, customers’ preferences toward various aspects of the product can be exploited by promising sentiment analysis methods. However, due to the complexity of language, state-of-the-art sentiment analysis methods are still not accurate for practice use in product redesign. To tackle this problem, we propose an integrated customer hearing and product redesign system, which combines the robust use of sentiment analysis for customer hearing and coordinated redesign mechanisms. Ontology and expert knowledges are involved to promote the accuracy. Specifically, a fuzzy product ontology that contains domain knowledges is first learned in a semi-supervised way. Then, UGC is exploited with a novel ontology-based fine-grained sentiment analysis approach. Extracted customer preference statistics are transformed into multilevels, for the automatic establishment of opportunity landscapes and house of quality table. Besides, customer preference statistics are interactively visualized, through which representative customer feedbacks are concurrently generated. Through a case study of smartphone, the effectiveness of the proposed system is validated, and applicable redesign strategies for a case product are provided. With this system, information including customer preferences, user experiences, using habits and conditions can be exploited together for reliable product redesign strategy elicitation.

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“…Most work on comparative relations focuses on certain languages, mainly English and Chinese [4,9,10,[15][16][17]. Other languages such as Arabic have been given less attention [10].…”

Section: Related Workmentioning

confidence: 99%

Sentiment analysis of Arabic comparative opinions

et al. 2019

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The rapid development of social media platforms allowed opinion mining research to increase significantly. Opinion mining/sentiment analysis is the process of extracting subjective information from opinions that imply single sentiment. Comparative opinion mining is a sub-field of opinion mining that deals with multi-sentiment opinions. Such opinions are expressed by comparing several entities to each other. The sentiment of a comparative relation is recognized by identifying the relation's direction and thus the preferred entity. This paper proposes an unsupervised sentiment analysis technique for Arabic comparative opinions to identify the preferred entity. The proposed technique considers three main elements when analyzing comparative opinions: The type of comparative keywords, the existence of features in the opinion, and the entities' position to the comparative keyword. Five main categories are proposed for classifying comparative keywords, which facilitates the analysis of each comparative sentence. The proposed technique limits the need for human interference to the initial steps of preparing the lexicons, collecting and categorizing comparative keywords. Furthermore, the proposed technique handles opinions that do not contain features at all. The results are very promising with a total average of 96.5% f-measure of correctly identified sentiment.

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Sentiment information extraction of comparative sentences based on CRF model

Cited by 12 publications

References 16 publications

An Algorithm for Automatic Recognition of Chinese Spatial Prepositional Phrases for Academic Texts

An Algorithm for Automatic Recognition of Chinese Spatial Prepositional Phrases for Academic Texts

Intelligent product redesign strategy with ontology-based fine-grained sentiment analysis

Sentiment analysis of Arabic comparative opinions

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