Word Sense Disambiguation using KeNet

Cetiner, Meltem; Yildirim, Ahmet; Onay, Bahadir; Oksuz, Cuneyt

doi:10.1109/siu53274.2021.9477816

Cited by 3 publications

(1 citation statement)

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“…Çetiner, Yıldırım, Onay and Öksüz [7] studied on resolving the ambiguities of multiple-sense words that have the same morphological structure. Turkish KeNet word network was used to capture the meaning of the words.…”

Section: Orhan and Altanmentioning

confidence: 99%

Improving the Performance of Sentiment Analysis by Ensemble Hybrid Learning Algorithm With NLP And Cascaded Feature Extraction

Altınel

ŞAHİN>

2023

International Journal of Advances in Engineering and Pure Sciences

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Sentiment analysis is a challenging problem in Natural Language Processing since every language has its own character within several difficulties such as ambiguity, synonymy, negative suffixes…etc. Since words with ambiguity can have different sentiment scores depending on the meaning they have in their corresponding context, we accomplished a study on Turkish language to determine whether the polarity scores of these polysemous words may differ according to their meaning. For a word with ambiguity, we first made a polarity calculation module to calculate its polarity score. This way, we calculated the polarity scores of 100 Turkish polysemous words. Then, since negation directly affects the correct meaning of the word in the sentiment analysis, a negation handler module is also implemented. After that, we prepared a sentiment polarity corpus which consists of 159,876 Turkish words including 100 Turkish polysemous words. Actually, the main purpose of this study is to detect sentiment polarity of Turkish texts by considering and building a specialized module for polysemous words. In short, we built a system for Turkish sentiment polarity detection task including these modules: 1) Pre-processing, 2) Polarity Calculation Module, 3) Negation Handling Module, 4) Feature Generation Module, and 5) Classification Module. According to our knowledge, this is the first study which includes all of these modules in one Turkish sentiment analysis task. Finally, we conducted this corpus using an ensemble hybrid regularized learning algorithm on two self-collected Twitter-datasets. Experimental results show that the suggested approach improves the classification performance on Turkish sentiment analysis task.

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Section: Orhan and Altanmentioning

confidence: 99%