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<h1 class="title is-1 publication-title">SentiWordNet for New Language: Automatic Translation Approach</h1> |
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<div class="is-size-5 publication-authors"> |
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<span class="author-block"> |
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<a href="https://aucan.github.io/" target="_blank">Alaettin Uçan</a><sup>1</sup>,</span> |
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<a href="https://profiles.stanford.edu/behzad-naderalvojoud" target="_blank">Behzad Naderalvojoud</a><sup>1</sup>,</span> |
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<span class="author-block"> |
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<a href="#">Ebru Akcapinar Sezer</a><sup>1</sup>, |
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<span class="author-block"> |
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<a href="#">Hayri Sever</a><sup>1</sup>, |
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<span class="author-block"><sup>1</sup>Hacettepe University</span> |
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<a href="https://ieeexplore.ieee.org/document/7907484" target="_blank" |
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<span>Paper</span> |
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<a href="https://gist.github.com/aucan/56c9100a74ee0d920440d89b1b7e152a" target="_blank" |
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<a href="https://huggingface.co/datasets/Alaettin/Humir-Sentiment-Datasets" target="_blank" |
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<span>Data</span> |
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<h2 class="title is-3">Abstract</h2> |
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<p> |
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This paper proposes an automatic translation approach to create a sentiment lexicon for a new language from available English resources. In this approach, an automatic mapping is generated from a sense-level resource to a wordlevel by applying a triple unification process. This process produces a single polarity score for each term by incorporating all sense polarities. The major idea is to deal with the sense ambiguity during the lexicon transfer and provide a general sentiment lexicon for languages like Turkish which do not have a freely available machine-readable dictionary. On the other hand, the translation quality is critical in the lexicon transfer due to the ambiguity problem. Thus, this paper also proposes a multiple bilingual translation approach to find the most appropriate equivalents for the source language terms. In this approach, three parallel, series and hybrid algorithms are used to integrate the translation results. Finally, three lexicons are achieved for the target language with different sizes. The performance of three lexicons is evaluated in the lexicon-based sentiment classification task and compared with the results achieved by the supervised approach. According to experimental results, the proposed approach can produce reliable sentiment lexicons for the target language. |
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<section class="section" id="BibTeX"> |
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<h2 class="title">BibTeX</h2> |
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<pre><code>@INPROCEEDINGS{7907484, |
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author={Ucan, Alaettin and Naderalvojoud, Behzad and Sezer, Ebru Akcapinar and Sever, Hayri}, |
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booktitle={2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)}, |
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title={SentiWordNet for New Language: Automatic Translation Approach}, |
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year={2016}, |
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volume={}, |
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number={}, |
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pages={308-315}, |
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keywords={Dictionaries;Sentiment analysis;Pragmatics;Semantics;Learning systems;Benchmark testing;Feature extraction;Sentiment lexicon;Translation approach;Machine learning;Sentiment analysis}, |
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doi={10.1109/SITIS.2016.57}} |
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</code></pre> |
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