SentiTurkNet: a Turkish polarity lexicon for sentiment analysis

Dehkharghani, Rahim; Saygın, Yücel; Yanıkoğlu, Berrin; Oflazer, Kemal

doi:10.1007/s10579-015-9307-6

Cited by 82 publications

(55 citation statements)

References 20 publications

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“…The authors claimed it as a first lexical resource that could assist in developing sentiment analysis systems in the Italian language. Dehkharghani et al (2016) in their work sentiment lexicons developed the first Turkish sentiment lexicon using semiautomatic approach by assigning sentiment scores to all synsets in the Turkish WordNet using SentiWordNet and pointwise mutual information technique. The results depicted that polarity scores assigned by their lexicon are more accurate as compared with baseline methods.…”

Section: Sentiment Lexicons In Englishmentioning

confidence: 99%

“…The aforementioned approaches for sentiment lexicon generation are widely used for creating sentiment to process English text. Several studies (Afraz, Muhammad, & Martinez-Enriquez, 2011;Awais, 2012;Badaro, Baly, Hajj, Habash, & El-Hajj, 2014;Bakliwal, Arora, & Varma, 2012;Dashtipour et al, 2016;Dehkharghani, Saygin, Yanikoglu, & Oflazer, 2016) have been conducted to perform sentiment analysis in languages other than English; till date, most of the research efforts made in the area of sentiment analysis deal with English text (Mukhtar et al, 2017). This is due to the fact that extraction and analysis of sentiments from text need a rich collection of lexical resources of that language.…”

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confidence: 99%

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Creating sentiment lexicon for sentiment analysis in Urdu: The case of a resource‐poor language

et al. 2019

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The sentiment analysis (SA) applications are becoming popular among the individuals and organizations for gathering and analysing user's sentiments about products, services, policies, and current affairs. Due to the availability of a wide range of English lexical resources, such as part‐of‐speech taggers, parsers, and polarity lexicons, development of sophisticated SA applications for the English language has attracted many researchers. Although there have been efforts for creating polarity lexicons in non‐English languages such as Urdu, they suffer from many deficiencies, such as lack of publically available sentiment lexicons with a proper scoring mechanism of opinion words and modifiers. In this work, we present a word‐level translation scheme for creating a first comprehensive Urdu polarity resource: “Urdu Lexicon” using a merger of existing resources: list of English opinion words, SentiWordNet, English–Urdu bilingual dictionary, and a collection of Urdu modifiers. We assign two polarity scores, positive and negative, to each Urdu opinion word. Moreover, modifiers are collected, classified, and tagged with proper polarity scores. We also perform an extrinsic evaluation in terms of subjectivity detection and sentiment classification, and the evaluation results show that the polarity scores assigned by this technique are more accurate than the baseline methods.

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Section: Sentiment Lexicons In Englishmentioning

confidence: 99%

mentioning

confidence: 99%

Creating sentiment lexicon for sentiment analysis in Urdu: The case of a resource‐poor language

et al. 2019

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“…Our polarity lexicon is the first comprehensive Turkish lexicon established by Dehkharghani et al (2014) by using several resources both in English as well as in Turkish SentiTurkNet. In building this lexicon, authors did not translate SentiWordNet to Turkish as done in (Türkmenoglu and Tantug, 2014), rather they established the lexicon using NLP techniques and available resources such as Turkish WordNet (Bilgin et al, 2004), English WordNet (Miller, 1995), SentiWordNet (Esuli and Sebastiani, 2006) and SenticNet (Cambria et al, 2010).…”

Section: Polarity Lexiconmentioning

confidence: 99%

“…To generate the lexicons of various sizes, we started with the polarity values of the seed words, obtained from the SentiTurkNet (Dehkharghani et al, 2014), then the rest of the new lexicon was filled by randomly choosing the necessary number of synsets from the lexicon. To obtain more robust results, we randomly chose the rest of the words in the new lexicon five times and obtained the results and computed an average over these.…”

Section: Lexicon Effectmentioning

confidence: 99%

Sentiment Analysis in Turkish

Gezi̇ci

Yanıkoğlu

2018

Turkish Natural Language Processing

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In this chapter, we give an overview of sentiment analysis problem and present a system to estimate the sentiment of movie reviews in Turkish. Our approach combines supervised learning and lexicon-based approaches, making use of a recently constructed Turkish polarity lexicon called SentiTurkNet. For performance evaluation, we investigate the contribution of different feature sets, as well as the effect of lexicon size on the overall classification performance.

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“…The effects of normalization, preprocessing, stemming, morphological analyses and POS tagging are tested and 73%-79% accuracy on the dataset with about 6% relative improvement by using empirically determined parameters are obtained. Dehkharghani et al [31] have semi-automatically generated a Turkish polarity resource, SentiTurkNet, and experimented on Turkish movie reviews. They have used three classifiers that are Logistic Regression (LR), Feedforward Neural Networks, and SVM with sequential minimal optimization algorithm and obtained 91.11% accuracy as the best when classifying synsets into the three polarities.…”

Section: Related Workmentioning

confidence: 99%

Türkçe ve İngilizce Yorumların Duygu Analizinde Doküman Vektörü Hesaplama Yöntemleri için Bir Deneysel İnceleme

Gözükara¹,

Özel²

2016

Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi

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Sentiment analysis is the task of identifying overall attitude of the given text documents by using text analysis and natural language processing techniques. In this study, we present experimental results of sentiment analysis on movie and product reviews datasets that are in Turkish and English languages by using a Support Vector Machine (SVM) classifier. Moreover, we compare different document vector computation techniques and show their effects on the sentiment analysis. We empirically evaluate SVM types, kernel types, weighting schemes such as TF or TF*IDF, TF variances, IDF variances, tokenization methods, feature selection systems, text preprocessing techniques and vector normalizations. We have obtained 91.33% accuracy as the best on our collected Turkish product reviews dataset by using C-SVC SVM type with linear kernel, log normalization TF* probabilistic IDF weighting scheme, L2 vector normalization, Chi-square feature selection, and unigram word tokenization. A very detailed comparison of the document vector computation methods over Turkish and English datasets are also presented.

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SentiTurkNet: a Turkish polarity lexicon for sentiment analysis

Cited by 82 publications

References 20 publications

Creating sentiment lexicon for sentiment analysis in Urdu: The case of a resource‐poor language

Creating sentiment lexicon for sentiment analysis in Urdu: The case of a resource‐poor language

Sentiment Analysis in Turkish

Türkçe ve İngilizce Yorumların Duygu Analizinde Doküman Vektörü Hesaplama Yöntemleri için Bir Deneysel İnceleme

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