<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="akhtar-etal-2017-iitp">
    <titleInfo>
        <title>IITP at EmoInt-2017: Measuring Intensity of Emotions using Sentence Embeddings and Optimized Features</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Md</namePart>
        <namePart type="given">Shad</namePart>
        <namePart type="family">Akhtar</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Palaash</namePart>
        <namePart type="family">Sawant</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Asif</namePart>
        <namePart type="family">Ekbal</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Jyoti</namePart>
        <namePart type="family">Pawar</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Pushpak</namePart>
        <namePart type="family">Bhattacharyya</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2017-09</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Alexandra</namePart>
            <namePart type="family">Balahur</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Saif</namePart>
            <namePart type="given">M</namePart>
            <namePart type="family">Mohammad</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Erik</namePart>
            <namePart type="family">van der Goot</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Copenhagen, Denmark</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>This paper describes the system that we submitted as part of our participation in the shared task on Emotion Intensity (EmoInt-2017). We propose a Long short term memory (LSTM) based architecture cascaded with Support Vector Regressor (SVR) for intensity prediction. We also employ Particle Swarm Optimization (PSO) based feature selection algorithm for obtaining an optimized feature set for training and evaluation. System evaluation shows interesting results on the four emotion datasets i.e. anger, fear, joy and sadness. In comparison to the other participating teams our system was ranked 5th in the competition.</abstract>
    <identifier type="citekey">akhtar-etal-2017-iitp</identifier>
    <identifier type="doi">10.18653/v1/W17-5229</identifier>
    <location>
        <url>https://aclanthology.org/W17-5229</url>
    </location>
    <part>
        <date>2017-09</date>
        <extent unit="page">
            <start>212</start>
            <end>218</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T IITP at EmoInt-2017: Measuring Intensity of Emotions using Sentence Embeddings and Optimized Features
%A Akhtar, Md Shad
%A Sawant, Palaash
%A Ekbal, Asif
%A Pawar, Jyoti
%A Bhattacharyya, Pushpak
%Y Balahur, Alexandra
%Y Mohammad, Saif M.
%Y van der Goot, Erik
%S Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F akhtar-etal-2017-iitp
%X This paper describes the system that we submitted as part of our participation in the shared task on Emotion Intensity (EmoInt-2017). We propose a Long short term memory (LSTM) based architecture cascaded with Support Vector Regressor (SVR) for intensity prediction. We also employ Particle Swarm Optimization (PSO) based feature selection algorithm for obtaining an optimized feature set for training and evaluation. System evaluation shows interesting results on the four emotion datasets i.e. anger, fear, joy and sadness. In comparison to the other participating teams our system was ranked 5th in the competition.
%R 10.18653/v1/W17-5229
%U https://aclanthology.org/W17-5229
%U https://doi.org/10.18653/v1/W17-5229
%P 212-218

Markdown (Informal)

[IITP at EmoInt-2017: Measuring Intensity of Emotions using Sentence Embeddings and Optimized Features](https://aclanthology.org/W17-5229) (Akhtar et al., WASSA 2017)

• IITP at EmoInt-2017: Measuring Intensity of Emotions using Sentence Embeddings and Optimized Features (Akhtar et al., WASSA 2017)

ACL

• Md Shad Akhtar, Palaash Sawant, Asif Ekbal, Jyoti Pawar, and Pushpak Bhattacharyya. 2017. IITP at EmoInt-2017: Measuring Intensity of Emotions using Sentence Embeddings and Optimized Features. In Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 212–218, Copenhagen, Denmark. Association for Computational Linguistics.