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        <title>Multi-entity sentiment analysis using entity-level feature extraction and word embeddings approach</title>
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        <namePart type="given">Colm</namePart>
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            <title>Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017</title>
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            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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        <name type="personal">
            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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    <abstract>The sentiment analysis task has been traditionally divided into lexicon or machine learning approaches, but recently the use of word embeddings methods have emerged, that provide powerful algorithms to allow semantic understanding without the task of creating large amounts of annotated test data. One problem with this type of binary classification, is that the sentiment output will be in the form of ‘1’ (positive) or ‘0’ (negative) for the string of text in the tweet, regardless if there are one or more entities referred to in the text. This paper plans to enhance the word embeddings approach with the deployment of a sentiment lexicon-based technique to appoint a total score that indicates the polarity of opinion in relation to a particular entity or entities. This type of sentiment classification is a way of associating a given entity with the adjectives, adverbs, and verbs describing it, and extracting the associated sentiment to try and infer if the text is positive or negative in relation to the entity or entities.</abstract>
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    <identifier type="doi">10.26615/978-954-452-049-6_094</identifier>
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%0 Conference Proceedings
%T Multi-entity sentiment analysis using entity-level feature extraction and word embeddings approach
%A Sweeney, Colm
%A Padmanabhan, Deepak
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017
%D 2017
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F sweeney-padmanabhan-2017-multi
%X The sentiment analysis task has been traditionally divided into lexicon or machine learning approaches, but recently the use of word embeddings methods have emerged, that provide powerful algorithms to allow semantic understanding without the task of creating large amounts of annotated test data. One problem with this type of binary classification, is that the sentiment output will be in the form of ‘1’ (positive) or ‘0’ (negative) for the string of text in the tweet, regardless if there are one or more entities referred to in the text. This paper plans to enhance the word embeddings approach with the deployment of a sentiment lexicon-based technique to appoint a total score that indicates the polarity of opinion in relation to a particular entity or entities. This type of sentiment classification is a way of associating a given entity with the adjectives, adverbs, and verbs describing it, and extracting the associated sentiment to try and infer if the text is positive or negative in relation to the entity or entities.
%R 10.26615/978-954-452-049-6_094
%U https://doi.org/10.26615/978-954-452-049-6_094
%P 733-740

Markdown (Informal)

[Multi-entity sentiment analysis using entity-level feature extraction and word embeddings approach](https://doi.org/10.26615/978-954-452-049-6_094) (Sweeney & Padmanabhan, RANLP 2017)

• Multi-entity sentiment analysis using entity-level feature extraction and word embeddings approach (Sweeney & Padmanabhan, RANLP 2017)

ACL

• Colm Sweeney and Deepak Padmanabhan. 2017. Multi-entity sentiment analysis using entity-level feature extraction and word embeddings approach. In Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017, pages 733–740, Varna, Bulgaria. INCOMA Ltd..