Author : Vrushali Rajendra Shete and Dr. Devendra P Gadekar

Analysis of post by use of emotions is challenge as they contain less contextual information. Emotions used in microblog environments are used many times and their meaning is clear in respect to emotions shown. As they form basis to importance for microblog emotion analysis. Earlier studies have overlooked emotions, emotional potentiality and have used emotions only as noisy emotion labels or indicators to train classifier. This issue is resolved by constructing a separate space for emoticon as emotional space which is represented as feature matrix and projections of emotions and words is done here. this is based on semantic composition. In this system we have proposed Emotion recognition on Twitter using Python. This is based on new Emotion Sematic Enhanced convolutional neural network model (ECNN) which improves the performance of emotion analysis. Emoticon embedding in emotional space as projection operator. Performance of emotion analysis is improved as proposed method is capable to capture more emotion sematic then other models which is done by projecting emoticons and words into emoticon space. This helps identify subjectivity, polarity and emotion in microblog environment. This paper in course gives insights for design of ECNN. Facial expressions give important clues about emotions. Sentimental analysis is other natural language processes for more task. Thus, many approaches have been put forth to classify human affective states In Facial expression analysis, features used are local spatial position or missing points or regions of face. In audio analysis, global statistics of acoustics is used as feature.

Keywords: Emotion Recognition, Twitter, Text Mining, Natural Language Processing (NLP), Hashtags, Sentiment Analysis, Convolution Neural Network