Multiclass Emotion Analysis Using NLP
International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064


Downloads: 217 | Views: 440

Research Paper | Computer Science & Engineering | India | Volume 7 Issue 11, November 2018 | Popularity: 6.8 / 10


     

Multiclass Emotion Analysis Using NLP

Rohan Madhani, Sagar Makwana, Viral Lakhani, Alabh Mehta, Sindhu Nair


Abstract: In the present scenario, sentiment analysis has become a popular topic in the field of Machine Learning (ML) and Natural Language Processing (NLP). Sentiment analysis is the systematic process of determining the sentimental tone in a array of words. It helps to understand the emotion, attitudes, and opinions expressed in the sentence. Machine learning techniques are widely used in determining the emotions from texts due to their precise prediction. Various classifiers can be used for performing sentiment analysis which may provide different accuracy. This paper documents a comparative study of three machine learning classifiers namely, Support Vector Machine (SVM), Recurrent Neural Network (RNN) - Long Short Term Memory (LSTM) and Naive Bayes for performing seven class sentiment analysis. The emotions which were considered for this study were: joy, sadness, anger, shame, guilt, disgust and fear. For analyzing the performance of these models precision, recall and F1 score were used. From the result, we come to know that the performance of a Recurrent Neural Network was much better than other classifiers


Keywords: Sentiment Analysis, Machine Learning, Support Vector Machine, Recurrent Neural Network, Naive Bayes


Edition: Volume 7 Issue 11, November 2018


Pages: 336 - 338



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Rohan Madhani, Sagar Makwana, Viral Lakhani, Alabh Mehta, Sindhu Nair, "Multiclass Emotion Analysis Using NLP", International Journal of Science and Research (IJSR), Volume 7 Issue 11, November 2018, pp. 336-338, https://www.ijsr.net/getabstract.php?paperid=ART20192599, DOI: https://www.doi.org/10.21275/ART20192599

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