Automatic construction of domain sentiment lexicon for semantic disambiguation

Wang, Yanyan; Yin, Fulian; Li, Jianbo; Tosato, Marco

doi:10.1007/s11042-020-09030-1

Cited by 20 publications

(11 citation statements)

References 32 publications

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“…However, dictionary-based approaches are easily affected by noise because of the polysemy of words, so it is difficult to achieve a good application effect. To solve these problems, Wang et al (2020) calculated the semantic distance between words to avoid sentimental ambiguity. Xing et al (2019) conducted an adaptive research of domain generalization and modified the polarity judgement of sentiment words in the application.…”

Section: Related Workmentioning

confidence: 99%

“…Methods commonly used for sentiment words extraction include rule-based extraction (Bhamare and Prabhu, 2021; Zhang et al , 2019), part-of-speech-based extraction (Mukhtar and Khan, 2019; Yin et al , 2020) and N-gram extraction (Keshavarz and Abadeh, 2017; Salas-Zárate et al , 2017). Focusing on sentiment words selection and polarity judgement, a variety of solutions have been proposed, such as the linguistic rules-based method; the words similarity-based method, including point mutual information (Wang et al , 2020; Wei et al , 2017), context similarity (Yu et al , 2013) and semantic similarity information derived from Word2vec (Araque et al , 2018; Liu et al , 2019); the graph-based method (Sheng et al , 2014); and the classification-based method (Han et al , 2018). The effect of constructing a sentiment lexicon based on corpus is affected by many factors, such as the scale and quality of the corpus, the influence of candidate sentiment word extraction strategy, sentiment word selection and the sentiment tendency judgement strategy.…”

Section: Related Workmentioning

confidence: 99%

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Sentiment lexicon construction for Chinese book reviews based on ultrashort reviews

Zha

Shi

2022

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Purpose Sentiment lexicon is an essential resource for sentiment analysis of user reviews. By far, there is still a lack of domain sentiment lexicon with large scale and high accuracy for Chinese book reviews. This paper aims to construct a large-scale sentiment lexicon based on the ultrashort reviews of Chinese books. Design/methodology/approach First, large-scale ultrashort reviews of Chinese books, whose length is no more than six Chinese characters, are collected and preprocessed as candidate sentiment words. Second, non-sentiment words are filtered out through certain rules, such as part of speech rules, context rules, feature word rules and user behaviour rules. Third, the relative frequency is used to select and judge the polarity of sentiment words. Finally, the performance of the sentiment lexicon is evaluated through experiments. Findings This paper proposes a method of sentiment lexicon construction based on ultrashort reviews and successfully builds one for Chinese books with nearly 40,000 words based on the Douban book. Originality/value Compared with the idea of constructing a sentiment lexicon based on a small number of reviews, the proposed method can give full play to the advantages of data scale to build a corpus. Moreover, different from the computer segmentation method, this method helps to avoid the problems caused by immature segmentation technology and an imperfect N-gram language model.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Sentiment lexicon construction for Chinese book reviews based on ultrashort reviews

Zha

Shi

2022

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“…Different Bag-of-Words (BOW) aspects have been investigated to detect sentiments [23]. Many studies have been conducted to determine lexicon words domain and to disambiguate their meaning based on fuzzy lexico-semantic and word meaning similarities [24][25][26].…”

Section: Previous Workmentioning

confidence: 99%

The construction of an accurate Arabic sentiment analysis system based on resources alteration and approaches comparison

Touahri

2022

ACI

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PurposeThis paper purposed a multi-facet sentiment analysis system.Design/methodology/approachHence, This paper uses multidomain resources to build a sentiment analysis system. The manual lexicon based features that are extracted from the resources are fed into a machine learning classifier to compare their performance afterward. The manual lexicon is replaced with a custom BOW to deal with its time consuming construction. To help the system run faster and make the model interpretable, this will be performed by employing different existing and custom approaches such as term occurrence, information gain, principal component analysis, semantic clustering, and POS tagging filters.FindingsThe proposed system featured by lexicon extraction automation and characteristics size optimization proved its efficiency when applied to multidomain and benchmark datasets by reaching 93.59% accuracy which makes it competitive to the state-of-the-art systems.Originality/valueThe construction of a custom BOW. Optimizing features based on existing and custom feature selection and clustering approaches.

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“…e concept of sentiment is considered based on the dictionary, and sentiment analysis is realized through sentiment embedding [4]. With the automatic construction of the domain dictionary, the context is considered, and thus the performance of the basic dictionary can be improved [5].…”

Section: Related Workmentioning

confidence: 99%

Multimodal Blog Sentiment Classification Based on MD-HFCE

Yang

Tian

2021

Security and Communication Networks

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In recent years, the rapid growth of multimodal information has become an important factor affecting the results of sentiment analysis. However, a few state-of-the-art works take into account the multimodal features and sentiment fuzziness. To this end, a fuzzy method is proposed for assessing sentiment intensity in this paper. Firstly, based on the visual-text conversion network (CNN-LSTM), as well as sentiment optimization through SentiBank and SentiBridge, the visual features are normalized to the text features. At the same time, the emotional features of the extracted audio will be predicted by the random forest algorithm. Subsequently, the sentiment characteristics are processed by dual hesitant fuzzification to form positive and negative sentiment intensity factors. Finally, a classification method, that is, MD-HFCE (multilayer dual hesitant fuzzy comprehensive evaluation), fuzzy comprehensive evaluation method improved by Mamdani fuzzy reasoning, is proposed to realize the multifeature fuzzy sentiment classification based on the comprehensive sentiment dictionary. The classification results are applicable to the topics of sentiment monitoring. The experimental results show that the proposed algorithm can effectively realize feature integration and improve the average sentiment classification accuracy of multimodal blogs to 82.2%.

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Automatic construction of domain sentiment lexicon for semantic disambiguation

Cited by 20 publications

References 32 publications

Sentiment lexicon construction for Chinese book reviews based on ultrashort reviews

Sentiment lexicon construction for Chinese book reviews based on ultrashort reviews

The construction of an accurate Arabic sentiment analysis system based on resources alteration and approaches comparison

Multimodal Blog Sentiment Classification Based on MD-HFCE

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