A Framework for Sentiment Analysis of Telugu Tweets
G. Balakrishna Priya1, M. Usha Rani2
1G. Balakrishna Priya*, Research Scholar, Department of Computer Science, Sri Padmavathi Mahila Viswa Vidyalayam, Tirupati (Andhra Pradesh), India.
2Prof. M. Usha Rani, Department of Computer Science, Sri Padmavathi Mahila Viswa Vidyalayam, Tirupati (Andhra Pradesh), India.
Manuscript received on August 07, 2020. | Revised Manuscript received on August 15, 2020. | Manuscript published on August 30, 2020. | PP: 523-525 | Volume-9 Issue-6, August 2020. | Retrieval Number: F1602089620/2020©BEIESP | DOI: 10.35940/ijeat.F1602.089620
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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: Now a day Social Media like Facebook, twitter and Instagram is major Sources for people to share their emotions based on the current situations in society. By knowing the interesting patterns in it, a government/appropriate person for that situation can take good and useful decisions. Sentiment analysis is a method where people can extract the useful information from the text like the emotions (happy, sad, and neutral) of people. Much research work was been underdoing in the area of sentiment analysis. Among that work the Machine learning and Deep learning approaches plays a maximum role. Existing works on sentiment analysis is going in the English language. In this paper, proposed a novel framework that specifically designed to do sentiment analysis of the text data, that available in the telugu language. The proposed framework was integrated with the word embedding model Word2Vec, language translator and deep learning approaches like Recurrent Neural Network and Navie base algorithms to collect and analyse the sentiment in tweeter data that present in telugu language. The results shows effective in terms of accuracy, precision and specificity.
Keywords: Sentiment Analysis, Framework, Twitter data, Telugu and Deep learning.