Sentiment Analysis at SEPLN (TASS)-2019: Sentiment Analysis at Tweet level using Deep Learning

Avishek Garain; Sainik Kumar Mahata

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Sentiment Analysis at SEPLN (TASS)-2019: Sentiment Analysis at Tweet level using Deep Learning

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arxiv 1908.00321 v1 pith:IZWN2NSR submitted 2019-08-01 cs.CL

Avishek Garain , Sainik Kumar Mahata This is my paper · ORCID

classification cs.CL

keywords analysissentimenttweetsdeepincludeslearningseplnsystem

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This paper describes the system submitted to "Sentiment Analysis at SEPLN (TASS)-2019" shared task. The task includes sentiment analysis of Spanish tweets, where the tweets are in different dialects spoken in Spain, Peru, Costa Rica, Uruguay and Mexico. The tweets are short (up to 240 characters) and the language is informal, i.e., it contains misspellings, emojis, onomatopeias etc. Sentiment analysis includes classification of the tweets into 4 classes, viz., Positive, Negative, Neutral and None. For preparing the proposed system, we use Deep Learning networks like LSTMs.

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