LSIS at SemEval-2016 Task 7: Using Web Search Engines for English and Arabic Unsupervised Sentiment Intensity Prediction

Amal Htait; Sébastien Fournier; Patrice Bellot

LSIS at SemEval-2016 Task 7: Using Web Search Engines for English and Arabic Unsupervised Sentiment Intensity Prediction

Amal Htait
, Sébastien Fournier
, Patrice Bellot

Research output: Chapter in Book/Published conference output › Conference publication

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Abstract

In this paper, we present our contribution in SemEval2016 task7 1 : Determining Sentiment Intensity of English and Arabic Phrases, where we use web search engines for English and Arabic unsupervised sentiment intensity prediction. Our work is based, first, on a group of classic sentiment lexicons (e.g. Sen-timent140 Lexicon, SentiWordNet). Second, on web search engines' ability to find the co-occurrence of sentences with predefined negative and positive words. The use of web search engines (e.g. Google Search API) enhance the results on phrases built from opposite polarity terms.

Original language	English
Title of host publication	International Workshop on Semantic Evaluation
Publisher	Association for Computational Linguistics
Publication status	Published - Jun 2016

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https://hal.archives-ouvertes.fr/hal-01771674/documentLicence: CC BY 4.0

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@inproceedings{d500eaf495f14d13af93f112715b1922,

title = "LSIS at SemEval-2016 Task 7: Using Web Search Engines for English and Arabic Unsupervised Sentiment Intensity Prediction",

abstract = "In this paper, we present our contribution in SemEval2016 task7 1 : Determining Sentiment Intensity of English and Arabic Phrases, where we use web search engines for English and Arabic unsupervised sentiment intensity prediction. Our work is based, first, on a group of classic sentiment lexicons (e.g. Sen-timent140 Lexicon, SentiWordNet). Second, on web search engines' ability to find the co-occurrence of sentences with predefined negative and positive words. The use of web search engines (e.g. Google Search API) enhance the results on phrases built from opposite polarity terms.",

author = "Amal Htait and S{\'e}bastien Fournier and Patrice Bellot",

year = "2016",

month = jun,

language = "English",

booktitle = "International Workshop on Semantic Evaluation",

publisher = "Association for Computational Linguistics",

}

TY - GEN

T1 - LSIS at SemEval-2016 Task 7: Using Web Search Engines for English and Arabic Unsupervised Sentiment Intensity Prediction

AU - Htait, Amal

AU - Fournier, Sébastien

AU - Bellot, Patrice

PY - 2016/6

Y1 - 2016/6

N2 - In this paper, we present our contribution in SemEval2016 task7 1 : Determining Sentiment Intensity of English and Arabic Phrases, where we use web search engines for English and Arabic unsupervised sentiment intensity prediction. Our work is based, first, on a group of classic sentiment lexicons (e.g. Sen-timent140 Lexicon, SentiWordNet). Second, on web search engines' ability to find the co-occurrence of sentences with predefined negative and positive words. The use of web search engines (e.g. Google Search API) enhance the results on phrases built from opposite polarity terms.

AB - In this paper, we present our contribution in SemEval2016 task7 1 : Determining Sentiment Intensity of English and Arabic Phrases, where we use web search engines for English and Arabic unsupervised sentiment intensity prediction. Our work is based, first, on a group of classic sentiment lexicons (e.g. Sen-timent140 Lexicon, SentiWordNet). Second, on web search engines' ability to find the co-occurrence of sentences with predefined negative and positive words. The use of web search engines (e.g. Google Search API) enhance the results on phrases built from opposite polarity terms.

UR - https://hal.archives-ouvertes.fr/hal-01771674

M3 - Conference publication

BT - International Workshop on Semantic Evaluation

PB - Association for Computational Linguistics

ER -

LSIS at SemEval-2016 Task 7: Using Web Search Engines for English and Arabic Unsupervised Sentiment Intensity Prediction

Abstract

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