Newspaper reports contain some attitudes in seemingly objective words. This corpus-based discourse analytical study,by comparing the use of semantic prosodies in the reports from China Daily and the Washington Post re...Newspaper reports contain some attitudes in seemingly objective words. This corpus-based discourse analytical study,by comparing the use of semantic prosodies in the reports from China Daily and the Washington Post relating the Belt and Road Initiative, interprets the news attitudes presented by the two media toward it. The analysis of the news discourse will be carried out firstly with Antconc, a corpus analysis software which enables us to observe the most frequent co-occurrences of the phrases the Belt and Road, One Belt and One Road, New Silk Road and the initiative; and then COCA(Corpus of Contemporary American English) is involved to analyze the semantic prosodies of these co-occurrences, on this basis the semantic prosodies of the above phrases are revealed. The analysis of the semantic prosodies of the above four phrases in the two corpora shows that China Daily hold a supportive attitude toward the Initiative, while the Washington Post hold a complex one.展开更多
Sentiment Analysis(SA)of natural language text is not only a challenging process but also gains significance in various Natural Language Processing(NLP)applications.The SA is utilized in various applications,namely,ed...Sentiment Analysis(SA)of natural language text is not only a challenging process but also gains significance in various Natural Language Processing(NLP)applications.The SA is utilized in various applications,namely,education,to improve the learning and teaching processes,marketing strategies,customer trend predictions,and the stock market.Various researchers have applied lexicon-related approaches,Machine Learning(ML)techniques and so on to conduct the SA for multiple languages,for instance,English and Chinese.Due to the increased popularity of the Deep Learning models,the current study used diverse configuration settings of the Convolution Neural Network(CNN)model and conducted SA for Hindi movie reviews.The current study introduces an Effective Improved Metaheuristics with Deep Learning(DL)-Enabled Sentiment Analysis for Movie Reviews(IMDLSA-MR)model.The presented IMDLSA-MR technique initially applies different levels of pre-processing to convert the input data into a compatible format.Besides,the Term Frequency-Inverse Document Frequency(TF-IDF)model is exploited to generate the word vectors from the pre-processed data.The Deep Belief Network(DBN)model is utilized to analyse and classify the sentiments.Finally,the improved Jellyfish Search Optimization(IJSO)algorithm is utilized for optimal fine-tuning of the hyperparameters related to the DBN model,which shows the novelty of the work.Different experimental analyses were conducted to validate the better performance of the proposed IMDLSA-MR model.The comparative study outcomes highlighted the enhanced performance of the proposed IMDLSA-MR model over recent DL models with a maximum accuracy of 98.92%.展开更多
The discrimination of synonyms has always been one of the great challenges for English learners.Taking assessment and evaluation as examples,this study analyses the similarities and differences of the two words,as wel...The discrimination of synonyms has always been one of the great challenges for English learners.Taking assessment and evaluation as examples,this study analyses the similarities and differences of the two words,as well as their usage from the perspectives of frequency,stylistics,collocation and semantic prosody with the help of British National Corpus,and demonstrates the importance of corpus retrieval tools in synonyms discrimination.Furthermore,this paper will give some suggestions for English learners and teachers in English vocabulary teaching.展开更多
In order to better apply the semantic prosody theory to distinguish synonyms,this article chooses to use analysis software and corpus to specifically compare and analyze the difference between“weichi(维持)”and“baoc...In order to better apply the semantic prosody theory to distinguish synonyms,this article chooses to use analysis software and corpus to specifically compare and analyze the difference between“weichi(维持)”and“baochi(保持)”.Although both“weichi(维持)”and“baochi(保持)”show neutral colors in the dictionary,through analysis of collocation words,it is found that“baochi(保持)”is often matched with positively colored words,so it has positive semantic characteristics,while“weichi(维持)”is more neutral and negative semantic prosody characteristics,and in the diachronic and synchronic word frequency survey,the word frequency of“baochi(保持)”is higher than that of“weichi(维持)”.展开更多
Topic modeling is a probabilistic model that identifies topics covered in text(s). In this paper, topics were loaded from two implementations of topic modeling, namely, Latent Semantic Indexing (LSI) and Latent Dirich...Topic modeling is a probabilistic model that identifies topics covered in text(s). In this paper, topics were loaded from two implementations of topic modeling, namely, Latent Semantic Indexing (LSI) and Latent Dirichlet Allocation (LDA). This analysis was performed in a corpus of 1000 academic papers written in English, obtained from PLOS ONE website, in the areas of Biology, Medicine, Physics and Social Sciences. The objective is to verify if the four academic fields were represented in the four topics obtained by topic modeling. The four topics obtained from Latent Semantic Indexing (LSI) and Latent Dirichlet Allocation (LDA) did not represent the four academic fields.展开更多
If the collocation of a word creates a semantic atmosphere in the context,it will infect the word,so that it has a certain semantic prosody.Based on corpus,this paper chooses ting(挺)and guai(怪)as a case study.This s...If the collocation of a word creates a semantic atmosphere in the context,it will infect the word,so that it has a certain semantic prosody.Based on corpus,this paper chooses ting(挺)and guai(怪)as a case study.This study combines description and interpretation,qualitative and quantitative analysis,and uses semantic prosody theory and related software technology to make a contrastive analysis of semantic prosody of degree adverbs ting(挺)and guai(怪)from synchronic and diachronic perspectives.We find that the semantic prosodies of ting(挺)and guai(怪)belong to positive prosody and negative prosody respectively,which are related to grammaticalization.展开更多
The collocations of lexical item are able to create a certain semantic atmosphere in the context,which will infect the lexical item and thus give it a certain semantic prosody.Based on the literature corpus,this paper...The collocations of lexical item are able to create a certain semantic atmosphere in the context,which will infect the lexical item and thus give it a certain semantic prosody.Based on the literature corpus,this paper takes the French degree adverb“très”as the object of study.Combining description and interpretation,qualitative and quantitative analysis,the research uses linguistic analysis software as well as semantic prosody theory to analyze its semantic prosody.The study found that“très”possesses mixed semantic prosody.展开更多
This corpus-based study cmploys statistical analysis to compare the use of non-compositional formulaic sequences by high proficiency Chinese learners and native students.The top 5 overused and underused patterns by Ch...This corpus-based study cmploys statistical analysis to compare the use of non-compositional formulaic sequences by high proficiency Chinese learners and native students.The top 5 overused and underused patterns by Chinese users are identified.Features of their usage point to a lack of register awareness as well as linguistic resources,while showing individual differences.展开更多
文摘Newspaper reports contain some attitudes in seemingly objective words. This corpus-based discourse analytical study,by comparing the use of semantic prosodies in the reports from China Daily and the Washington Post relating the Belt and Road Initiative, interprets the news attitudes presented by the two media toward it. The analysis of the news discourse will be carried out firstly with Antconc, a corpus analysis software which enables us to observe the most frequent co-occurrences of the phrases the Belt and Road, One Belt and One Road, New Silk Road and the initiative; and then COCA(Corpus of Contemporary American English) is involved to analyze the semantic prosodies of these co-occurrences, on this basis the semantic prosodies of the above phrases are revealed. The analysis of the semantic prosodies of the above four phrases in the two corpora shows that China Daily hold a supportive attitude toward the Initiative, while the Washington Post hold a complex one.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2023R161)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:22UQU4340237DSR51).
文摘Sentiment Analysis(SA)of natural language text is not only a challenging process but also gains significance in various Natural Language Processing(NLP)applications.The SA is utilized in various applications,namely,education,to improve the learning and teaching processes,marketing strategies,customer trend predictions,and the stock market.Various researchers have applied lexicon-related approaches,Machine Learning(ML)techniques and so on to conduct the SA for multiple languages,for instance,English and Chinese.Due to the increased popularity of the Deep Learning models,the current study used diverse configuration settings of the Convolution Neural Network(CNN)model and conducted SA for Hindi movie reviews.The current study introduces an Effective Improved Metaheuristics with Deep Learning(DL)-Enabled Sentiment Analysis for Movie Reviews(IMDLSA-MR)model.The presented IMDLSA-MR technique initially applies different levels of pre-processing to convert the input data into a compatible format.Besides,the Term Frequency-Inverse Document Frequency(TF-IDF)model is exploited to generate the word vectors from the pre-processed data.The Deep Belief Network(DBN)model is utilized to analyse and classify the sentiments.Finally,the improved Jellyfish Search Optimization(IJSO)algorithm is utilized for optimal fine-tuning of the hyperparameters related to the DBN model,which shows the novelty of the work.Different experimental analyses were conducted to validate the better performance of the proposed IMDLSA-MR model.The comparative study outcomes highlighted the enhanced performance of the proposed IMDLSA-MR model over recent DL models with a maximum accuracy of 98.92%.
文摘The discrimination of synonyms has always been one of the great challenges for English learners.Taking assessment and evaluation as examples,this study analyses the similarities and differences of the two words,as well as their usage from the perspectives of frequency,stylistics,collocation and semantic prosody with the help of British National Corpus,and demonstrates the importance of corpus retrieval tools in synonyms discrimination.Furthermore,this paper will give some suggestions for English learners and teachers in English vocabulary teaching.
文摘In order to better apply the semantic prosody theory to distinguish synonyms,this article chooses to use analysis software and corpus to specifically compare and analyze the difference between“weichi(维持)”and“baochi(保持)”.Although both“weichi(维持)”and“baochi(保持)”show neutral colors in the dictionary,through analysis of collocation words,it is found that“baochi(保持)”is often matched with positively colored words,so it has positive semantic characteristics,while“weichi(维持)”is more neutral and negative semantic prosody characteristics,and in the diachronic and synchronic word frequency survey,the word frequency of“baochi(保持)”is higher than that of“weichi(维持)”.
文摘Topic modeling is a probabilistic model that identifies topics covered in text(s). In this paper, topics were loaded from two implementations of topic modeling, namely, Latent Semantic Indexing (LSI) and Latent Dirichlet Allocation (LDA). This analysis was performed in a corpus of 1000 academic papers written in English, obtained from PLOS ONE website, in the areas of Biology, Medicine, Physics and Social Sciences. The objective is to verify if the four academic fields were represented in the four topics obtained by topic modeling. The four topics obtained from Latent Semantic Indexing (LSI) and Latent Dirichlet Allocation (LDA) did not represent the four academic fields.
文摘If the collocation of a word creates a semantic atmosphere in the context,it will infect the word,so that it has a certain semantic prosody.Based on corpus,this paper chooses ting(挺)and guai(怪)as a case study.This study combines description and interpretation,qualitative and quantitative analysis,and uses semantic prosody theory and related software technology to make a contrastive analysis of semantic prosody of degree adverbs ting(挺)and guai(怪)from synchronic and diachronic perspectives.We find that the semantic prosodies of ting(挺)and guai(怪)belong to positive prosody and negative prosody respectively,which are related to grammaticalization.
文摘The collocations of lexical item are able to create a certain semantic atmosphere in the context,which will infect the lexical item and thus give it a certain semantic prosody.Based on the literature corpus,this paper takes the French degree adverb“très”as the object of study.Combining description and interpretation,qualitative and quantitative analysis,the research uses linguistic analysis software as well as semantic prosody theory to analyze its semantic prosody.The study found that“très”possesses mixed semantic prosody.
文摘This corpus-based study cmploys statistical analysis to compare the use of non-compositional formulaic sequences by high proficiency Chinese learners and native students.The top 5 overused and underused patterns by Chinese users are identified.Features of their usage point to a lack of register awareness as well as linguistic resources,while showing individual differences.
