Abstract
Emotion can be expressed in many ways that can be seen such as facial expression and gestures, speech and by written text. Emotion Detection in text documents is essentially a content– based classification problem involving concepts from the domains of Natural Language Processing as well as Machine Learning. In this paper we are proposing a solution for emotion recognition based on textual data. The emotion expressed in a blog, review or any kind of textual content remains unused until the text is analyzed and the emotion is retrieved from the data. It is impossible to analyze the huge amount of data manually and gain information from it. So, we are proposing a Model that will analyze the data and make a prediction of the emotion embedded into the textual data. We are using algorithms from the domain of Natural Language Processing and Machine Learning. Initially we will take some text as input and in next step we perform tokenization to the input text. Words related to emotions will be identified in the next step afterwards analysis of the intensity of emotion words will be performed. Sentence is checked whether negation is involved in it or not, then finally an emotion class will be found as the required output. In short Emotion Detection is the most important field of research in humancomputer interaction.
Recommended Citation
Banerjee, Subhodeep; Goswami, Shrivasta; Das, Arnab; Saha, Neeloy; Seth, Soumyashree; Bhattacharya, Sagnik; and Mandal, Dr. Sandip
(2024)
"A Novel Approach for Emotion Detection from Text Data using Natural Language Processing and Machine Learning,"
American Journal of Electronics & Communication (AJEC): Vol. 2:
Iss.
1, Article 5.
Available at:
https://research.smartsociety.org/ajec/vol2/iss1/5