Deep LSTM for Emoji Twitter Sentiment Exploration via Distributed Embeddings
Anupama Angadi1, Satish Muppidi2, Satya Keerthi Gorripati3

1Anupama Angadi, Department of CSE, Raghu Engineering College (Autonomous), Dakamarri, Visakhapatnam, 531162, A.P, India.
2Satish Muppidi, Department of IT, GMRIT, Rajam, India.
3Satya Keerthi Gorripati, Department of CSE, Gayatri Vidya Parishad College of Engineering (Autonomous), Visakhapatnam, A.P, India.

Manuscript received on 26 August 2019. | Revised Manuscript received on 06 September 2019. | Manuscript published on 30 September 2019. | PP: 1714-1718 | Volume-8 Issue-11, September 2019. | Retrieval Number: K15210981119/2019©BEIESP | DOI: 10.35940/ijitee.K1521.0981119
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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 (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Social media’s sentimental data is the most vital digital marketing platform that can help us to reveal the real world events including qualitative insights to understand people’s visibility about brands, politics, emotional status, and so on. With today’s interrelated world, a public relations disaster can be initiated with one post or a tweet. Conventional sentimental analysis is the process of defining whether the shared post on social media is neutral, positive or negative and has been focused by the Dealers, Administrations to understand public feelings of their products and corporation. However, extensive usage of emoji in social media has attracted an increasing interest. In this proposed framework, we suggest a novel scheme for Twitter sentiment method on emojis by considering pre-trained word and emoji embeddings. We first train our model to learn word, emoji embeddings under positive and negative tweets; later a classifier passes them through a neural network combining LSTM to achieve better performance. Our tests show that the proposed model operational for extracting sentiment-aware emojis and outperforms the state-of-the-art simulations.
Keywords: Emoji, distributed embeddings, LSTM, classifier, opinion mining.
Scope of the Article: