Emotion analysis using social media text is the emerging research area now a day. It helps the researcher to recognize the emotional state of the users and identify mental health-relevant problems like depression or anxiety, which may lead to suicide if not cured. The social media platforms like WhatsApp, Facebook, Instagram, etc. are widely used as these applications provide an affordable and reliable medium for transferring data, sharing thoughts, and even for routine informal communication.

more »

... ocial media status is normally analyzed to recognize the mood, emotion, thought process, or mental state of the individual as people generally share status for what they feel. On the other hand, pre-processing is the crucial step for any kind of text data analysis. In this paper, the social media status dataset is first pre-processed using various methods, given for feature extraction and classification purpose. For the machine learning approach, we have used count vectors and TFIDF techniques for extracting the different features of the data. Using count vector feature extraction accuracy achieved by pre-processed data is 68.90%, 69.33%, 70.59%, 64.95%, 69.33% for naïve Bayes, LDA, Random forest, SGD and MLP respectively. Similarly, using TF-IDF feature extraction accuracy achieved by pre-processed data is 65.76%, 69.96%, 68.49%, 65.96%, 70.80% for naïve Bayes, LDA, Random forest, SGD and MLP respectively. The experimental results show that preprocessing helps to improve the accuracy of the classifier and CNN outperforms the traditional approach and achieves 79% accuracy.