Multilabel Classification for Emotion Analysis of Multilingual Tweets
Lata Gohil1, Dharmendra Patel2
1Lata Gohil, Computer Science and Engineering Department, Institute of Technology, Nirma University, India
2Smt. Chandaben Mohanbhai Patel Institute of Computer Applications (CMPICA), CHARUSAT, Changa, India
3Dharmendra Patel, Smt. Chandaben Mohanbhai Patel Institute of Computer Applications (CMPICA), CHARUSAT, Changa, India
Manuscript received on October 12, 2019. | Revised Manuscript received on 22 October, 2019. | Manuscript published on November 10, 2019. | PP: 4453-4357 | Volume-9 Issue-1, November 2019. | Retrieval Number: A5320119119/2019©BEIESP | DOI: 10.35940/ijitee.A5320.119119
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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: Emotion Analysis of text targets to detect and recognize types of feelings expressed in text. Emotion analysis is successor of Sentiment analysis. The latter does coarse-level analysis and classify the text into positive and negative categories while former does fine-grain analysis and classify text in specific emotion categories like happy, surprise, angry. Analysis of text at fine-level provides deeper insight compared to coarse-level analysis. In this paper, tweets are classified in discrete eight basic emotions namely joy, trust, fear, surprise, sadness, anticipation, anger, disgust specified in Plutchik’s wheel of emotions [1]. Tweets for three languages collected out of which one is English language and rest two are Indian languages namely Gujarati and Hindi. The collected tweets are related to Indian politics and are annotated manually. Supervised Learning and Hybrid approach are used for classification of tweets. Supervised learning uses tf-idf as features while hybrid approach uses primary and secondary features. Primary features are generated using tf-idf weighting and two different algorithms of feature generation are proposed which generate secondary features using SenticNet resource. Multilabel classification is performed to classify tweets in emotion categories. Results of experiments show effectiveness of hybrid approach.
Keywords: Emotion Analysis, Sentiment Analysis, Affect Analysis, Fine-grained, Hindi Corpus, Gujarati Corpus, Opinion Mining
Scope of the Article: Classification