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        <title>Sentiment Intensity Ranking among Adjectives Using Sentiment Bearing Word Embeddings</title>
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    <name type="personal">
        <namePart type="given">Raksha</namePart>
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        <namePart type="family">Somani</namePart>
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    <name type="personal">
        <namePart type="given">Lakshya</namePart>
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    <name type="personal">
        <namePart type="given">Pushpak</namePart>
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            <title>Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing</title>
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    <abstract>Identification of intensity ordering among polar (positive or negative) words which have the same semantics can lead to a fine-grained sentiment analysis. For example, ‘master’, ‘seasoned’ and ‘familiar’ point to different intensity levels, though they all convey the same meaning (semantics), i.e., expertise: having a good knowledge of. In this paper, we propose a semi-supervised technique that uses sentiment bearing word embeddings to produce a continuous ranking among adjectives that share common semantics. Our system demonstrates a strong Spearman’s rank correlation of 0.83 with the gold standard ranking. We show that sentiment bearing word embeddings facilitate a more accurate intensity ranking system than other standard word embeddings (word2vec and GloVe). Word2vec is the state-of-the-art for intensity ordering task.</abstract>
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        <date>2017-09</date>
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%0 Conference Proceedings
%T Sentiment Intensity Ranking among Adjectives Using Sentiment Bearing Word Embeddings
%A Sharma, Raksha
%A Somani, Arpan
%A Kumar, Lakshya
%A Bhattacharyya, Pushpak
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F sharma-etal-2017-sentiment
%X Identification of intensity ordering among polar (positive or negative) words which have the same semantics can lead to a fine-grained sentiment analysis. For example, ‘master’, ‘seasoned’ and ‘familiar’ point to different intensity levels, though they all convey the same meaning (semantics), i.e., expertise: having a good knowledge of. In this paper, we propose a semi-supervised technique that uses sentiment bearing word embeddings to produce a continuous ranking among adjectives that share common semantics. Our system demonstrates a strong Spearman’s rank correlation of 0.83 with the gold standard ranking. We show that sentiment bearing word embeddings facilitate a more accurate intensity ranking system than other standard word embeddings (word2vec and GloVe). Word2vec is the state-of-the-art for intensity ordering task.
%R 10.18653/v1/D17-1058
%U https://aclanthology.org/D17-1058/
%U https://doi.org/10.18653/v1/D17-1058
%P 547-552

Markdown (Informal)

[Sentiment Intensity Ranking among Adjectives Using Sentiment Bearing Word Embeddings](https://aclanthology.org/D17-1058/) (Sharma et al., EMNLP 2017)

• Sentiment Intensity Ranking among Adjectives Using Sentiment Bearing Word Embeddings (Sharma et al., EMNLP 2017)

ACL

• Raksha Sharma, Arpan Somani, Lakshya Kumar, and Pushpak Bhattacharyya. 2017. Sentiment Intensity Ranking among Adjectives Using Sentiment Bearing Word Embeddings. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 547–552, Copenhagen, Denmark. Association for Computational Linguistics.