An Efficient Framework for Detecting Various Moods in Hinglish and English Dataset
Vikas Tripathi1, Himanshu Silswal2, Gaurav Rawat3, Tanmay Jain4
1Vikas Tripathi*, Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, India.
2Himanshu Silswal, Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, India.
3Gaurav Rawat, Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, India.
4Tanmay Jain, Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, India.
Manuscript received on November 13, 2019. | Revised Manuscript received on 22 November, 2019. | Manuscript published on December 10, 2019. | PP: 1943-1946 | Volume-9 Issue-2, December 2019. | Retrieval Number: B7846129219/2019©BEIESP | DOI: 10.35940/ijitee.B7846.129219
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© 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: Due to the paced growth in web technologies and natural language processing, research on Sentiment Analysis (SA) has become very popular in recent times. In recent years most of the research papers have focused on sentiment analysis based on polarity (positive and negative sentiments). This paper presents an effective framework for identification of various moods of person from its written text or sentences. The paper focuses on the mood detection in given text written in mixed language called “Hinglish”. Hinglish is actually a fusion of two languages, English with the Hindi language. The major goal of this research is to propose a methodology for extracting information of emotions from a given text in Hinglish. The framework tested on 700 sentences containing Hinglish data. Seven emotions anger, happy, joy, confidence, sadness, tentativeness and fear have been used for generation of results. The proposed approach yielded an accuracy of 93.96%.
Keywords: Hinglish, Mixed Language, Natural Language Processing, Sentiment Analysis.
Scope of the Article: Natural Language Processing