Sentence Level Sentiment Analysis using Deep Learning Method
K.K. Uma1, K. Meenakshisundaram2

1K.K. Uma, Research Scholar, Department of Computer Science, Erode Arts and Science College, Erode, Tamilnadu, India.
2Dr. K. Meenakshisundaram, Associate professor, Department of Computer Science, Erode Arts and Science College, Erode, Tamilnadu, India.

Manuscript received on 01 August 2019. | Revised Manuscript received on 07 August 2019. | Manuscript published on 30 September 2019. | PP: 8453-8458 | Volume-8 Issue-3 September 2019 | Retrieval Number: C6467098319/2019©BEIESP | DOI: 10.35940/ijrte.C6467.098319

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Abstract: An Emoji is a small image representing facial expression, entity or a concept that can be either static or animated. In this paper, Emojis are used to study both cross-language and language based sentiment patterns. All the languages do not come with fair amount of labels. Emojis are useful signs of sentiment analysis in cross-lingual tweets. In this paper, an approach is proposed to extend the existing binary sentiment classification using multi-way classification. A novel Long Short-Term Memory (LSTM) – Convolutional Neural Networks (CNN) based model is proposed to obtain sentiments from emojis. The sentiments are classified using Deep Learning method like CNN. The proposed system outperforms the existing system in terms of Accuracy, Precision, Recall, F-Measure and Time Period. Finally the researcher manifests the fact that the CNN and LSTM combination as model shows an immense improvement to detecting the sentiment targets.
KEYWORDS: Emoji, Sentiment Analysis, Long Short-Term Memory (LSTM), Cross-Language

Scope of the Article: Deep Learning