Constructing Sentiment Lexicons in Norwegian from a Large Text Corpus

Bai, Aleksander; Hammer, Hugo Lewi; Yazidi, Anis; Engelstad, Paal E.

doi:10.1109/cse.2014.73

Cited by 14 publications

(14 citation statements)

References 7 publications

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“…They achieved different accuracies in different domain in English language. The concept of semantic orientation had been used in literature in various applications such as in [9] [10] [8]. In our work, we didn't use SO as it is in [8].…”

Section: Related Workmentioning

confidence: 99%

“…This weight is different from the polarity values that had found out in semantic orientation (SO) as adopted in [8] or [9], since OS is used for constructing sentiment lexicons form seeds predefined polarity terms [9], or for determining text polarity as in [8], whereas we used this weight as feature selection method for SA. Moreover, we had used in SKCM distinct way to find terms weight, than mathematical way in [8].…”

Section: Skcm Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Enhancing the Performance of Sentiment Analysis Supervised Learning Using Sentiments Keywords Based Technique

Abdelwahab

Alqasemi

Abdelkader

2017

Computer Science &Amp; Information Technology (CS &Amp; IT)

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Section: Related Workmentioning

confidence: 99%

Section: Skcm Algorithmmentioning

confidence: 99%

Enhancing the Performance of Sentiment Analysis Supervised Learning Using Sentiments Keywords Based Technique

Abdelwahab

Alqasemi

Abdelkader

2017

Computer Science &Amp; Information Technology (CS &Amp; IT)

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“…The computed PMI scores lay the foundation for the sentiment lexicon. For more details, see [18]. We denote this lexicon PMI in the rest of the paper.…”

Section: Preexisting Sentiment Lexiconsmentioning

confidence: 99%

Building Domain Specific Sentiment Lexicons Combining Information from Many Sentiment Lexicons and a Domain Specific Corpus

Hammer

Yazidi

Bai

et al. 2015

IFIP Advances in Information and Communication Technology

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“…According to [1,[13][14][15][16][17][18], Pointwise Mutual Information (PMI) between two words wi and wj has the equation P M I(w i , w j ) = log 2 P (w i , w j ) P (w i ) × P (w j )…”

Section: Creating the Sentiment Lexicons Of The Besdmentioning

confidence: 99%

“…In the research [1], the authors generate several Norwegian sentiment lexicons by extracting sentiment information from two different types of Norwegian text corpus, namely, news corpus and discussion forums. The methodology is based on the Point wise Mutual Information (PMI),…”

Section: Creating the Sentiment Lexicons Of The Besdmentioning

confidence: 99%

Latent Semantic Analysis using a Dennis Coefficient for English Sentiment Classification in a Parallel System

Ngoc

2018

INT J COMPUT COMMUN

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Abstract:We have already survey many significant approaches for many years because there are many crucial contributions of the sentiment classification which can be applied in everyday life, such as in political activities, commodity production, and commercial activities. We have proposed a novel model using a Latent Semantic Analysis (LSA) and a Dennis Coefficient (DNC) for big data sentiment classification in English. Many LSA vectors (LSAV) have successfully been reformed by using the DNC. We use the DNC and the LSAVs to classify 11,000,000 documents of our testing data set to 5,000,000 documents of our training data set in English. This novel model uses many sentiment lexicons of our basis English sentiment dictionary (bESD). We have tested the proposed model in both a sequential environment and a distributed network system. The results of the sequential system are not as good as that of the parallel environment. We have achieved 88.76% accuracy of the testing data set, and this is better than the accuracies of many previous models of the semantic analysis. Besides, we have also compared the novel model with the previous models, and the experiments and the results of our proposed model are better than that of the previous model. Many different fields can widely use the results of the novel model in many commercial applications and surveys of the sentiment classification.

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Constructing Sentiment Lexicons in Norwegian from a Large Text Corpus

Cited by 14 publications

References 7 publications

Enhancing the Performance of Sentiment Analysis Supervised Learning Using Sentiments Keywords Based Technique

Enhancing the Performance of Sentiment Analysis Supervised Learning Using Sentiments Keywords Based Technique

Building Domain Specific Sentiment Lexicons Combining Information from Many Sentiment Lexicons and a Domain Specific Corpus

Latent Semantic Analysis using a Dennis Coefficient for English Sentiment Classification in a Parallel System

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