Dominant Lexicon Based Bi-LSTM for Emotion Prediction on a Text
A. Shunmuga Sundari1, R. Shenbagavalli2

1A. Shunmuga Sundari, Part Time Ph.D Research Scholar, Rani Anna Government College for Women,Tirunelveli, India.

2Dr. R. Shenbagavalli, Assistant Professor, Rani Anna Government College for Women,Tirunelveli, India.

Manuscript received on 15 September 2019 | Revised Manuscript received on 23 September 2019 | Manuscript Published on 11 October 2019 | PP: 1272-1277 | Volume-8 Issue-11S September 2019 | Retrieval Number: K125609811S19/2019©BEIESP | DOI: 10.35940/ijitee.K1256.09811S19

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (

Abstract: User-generated content and opinionative data has become a massive source of information on World Wide Web in the past few decades. Through social media people can share more conveniently their opinions, views, feelings and attitude about a product, person or event at anytime and anywhere as daily basis. This ever-growing subjective data makes enormous amount of unstructured data in web. Analyzing emotion in this raw unstructured data gives a very fruitful information for any kind of decision making process taken by both government and industries. Sentiment or emotion analysis is a field of Natural Language Processing (NLP), is used to identify the emotion depicted (by) in the form of text. Computation of emotion and emotion intensity depicted by a text is a very difficult task. Feature extraction from the text for vector representation is a difficult step of emotion analysis because it defines the emotion accuracy of the prediction. In this paper, a selective lexicon based BI-LSTM technique has been proposed. This technique uses only the most affected lexicon and its features for final vector representation. This method is a combination of features collected from the convolutional Neural Network (CNN), Long Short Term Memory (Conv – LSTM) and Bidirectional Long Short Term Memory (BI-LSTM). As a result the proposed model Selective Lexicon Based BI-LSTM (SL + BI-LSTM) outperforms all the models with high accuracy.

Keywords: Sentiment analysis, Natural Language Processing, CNN, LSTM, BI-LSTM
Scope of the Article: Regression and Prediction