A Training‐Optimization‐Based Method for Constructing Domain‐Specific Sentiment Lexicon

Du, Maokang; Li, Xiaoguang; Luo, Longyan

doi:10.1155/2021/6152494

Cited by 7 publications

(7 citation statements)

References 24 publications

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“…Statistical information can be used to select and judge the polarity of sentiment words because of the idea that the sentiment polarity of words in the reviews is closely related to the polarity of the reviews. If the probability that a sentiment word appears in the positive reviews is greater than its probability in the negative reviews, the polarity of the sentiment word is also positive with a high probability, and vice versa (Du et al , 2021). The model proposed in this paper is also based on this idea.…”

Section: Methodsmentioning

confidence: 99%

“…Many types of research have proven that sentiment words have obvious domain dependence (Mishev et al , 2020; Park et al , 2015; Peng et al , 2017). Different domains have their special sentiment words, and the polarity of the same sentiment word may change in different domains in some cases (Du et al , 2021). For example, terms like copious annotations and acrid language are common in various books but are rarely seen in the comments about hotels and electronic products.…”

Section: Introductionmentioning

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: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sentiment lexicon construction for Chinese book reviews based on ultrashort reviews

Zha

Shi

2022

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“…Then, lexicon-based methods calculate the polarity scanning through the documents for these keywords. Some linguistic phenomena, such as polysemy or ambiguity, can hamper the performance of this approach but their effects can be lessened with the use of domain specific lexicons [16], [17]. On the other hand, the machine learning-based approach consists of training a model to discriminate between positive, neutral, and negative texts.…”

Section: A Sentiment Analysismentioning

confidence: 99%

Smart Analysis of Economics Sentiment in Spanish Based on Linguistic Features and Transformers

2023

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Texts related to economics and finances are characterized by the use of words and expressions whose meaning (and the sentiments they convey) substantially depend on the context. This poses a major challenge to Natural Language Processing tasks in general, and Sentiment Analysis in particular. For lowresource languages such as Spanish, this situation becomes even more acute. Yet, the latest advancements in the field, including word embeddings and transformers, have allowed to boost the performance of Sentiment Analysis solutions. In this work we explore the impact of the combination of different feature sets in the accuracy of Sentiment Analysis in Spanish financial texts. For this, a corpus with 15,915 tweets has been compiled and manually annotated as either positive, negative, or neutral. Then, feature sets based on contextual and non-contextual embeddings along with linguistic features were evaluated both individually and combined. The best results, with a weighted F1-score of 73.15880%, were obtained with a combination of feature sets by means of knowledge integration.

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“…In [42], authors discussed sentiment analysis of Urdu language by evaluating around 14 works of SA. They divided all Urdu SA techniques into different types: lexicon-based, machine learning, and hybrid techniques.…”

Section: Literature Reviewmentioning

confidence: 99%

“…An important part of the design of sentiment analysis algorithms are the words and phrases that make up the "sentiment lexicon" [42]. A sentiment class and score are given to each word, sentence, and document to facilitate in the calculation of the score at different levels, such as word, sentence and [21].…”

Section: Literature Reviewmentioning

confidence: 99%

A machine learning approach for Urdu text sentiment analysis

Akhtar

Rehman

2023

Mehran Univ. res. j. eng. technol.

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Product evaluations, ratings, and other sorts of online expressions have risen in popularity as a result of the emergence of social networking sites and blogs. Sentiment analysis has emerged as a new area of study for computational linguists as a result of this rapidly expanding data set. From around a decade ago, this has been a topic of discussion for English speakers. However, the scientific community completely ignores other important languages, such as Urdu. Morphologically, Urdu is one of the most complex languages in the world. For this reason, a variety of unique characteristics, such as the language's unusual morphology and unrestricted word order, make the Urdu language processing a difficult challenge to solve. This research provides a new framework for the categorization of Urdu language sentiments. The main contributions of the research are to show how important this multidimensional research problem is as well as its technical parts, such as the parsing algorithm, corpus, lexicon, etc. A new approach for Urdu text sentiment analysis including data gathering, pre-processing, feature extraction, feature vector formation, and finally, sentiment classification has been designed to deal with Urdu language sentiments. The result and discussion section provides a comprehensive comparison of the proposed work with the standard baseline method in terms of precision, recall, f-measure, and accuracy of three different types of datasets. In the overall comparison of the models, the proposed work shows an encouraging achievement in terms of accuracy and other metrics. Last but not least, this section also provides the featured trend and possible direction of the current work.

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A Training‐Optimization‐Based Method for Constructing Domain‐Specific Sentiment Lexicon

Cited by 7 publications

References 24 publications

Sentiment lexicon construction for Chinese book reviews based on ultrashort reviews

Sentiment lexicon construction for Chinese book reviews based on ultrashort reviews

Smart Analysis of Economics Sentiment in Spanish Based on Linguistic Features and Transformers

A machine learning approach for Urdu text sentiment analysis

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