Text Analysis
Expression of emotion in text is a complex phenomenon. In such a way that the shortest phrase can express multiple emotions with different intensity and that can be difficult to be understood at first glance. To overcome this issue, our project focuses on the complexity of emotional language and can detect multiple emotions from input sentences with intensity of each emotion.
Sentiment Analysis
Sentiment Analysis is the most common text classification tool that analyzes incoming messages, provides in-depth understanding and detects in the text whether the underlying sentiment is positive, negative or neutral. Sentiment Analysis is already a challenge due to the subjectivity of language and phenomena such as irony or sarcasm.Emotion Analysis
Emotion detection in computational linguistics is the process of identifying discrete emotions expressed in text. Emotion analysis can be viewed as a natural evolution of sentiment analysis and its more fine-grained model. Emotion analysis steps up the game by analyzing the user’s intention behind a message and identifying whether it is ‘sadness’, 'surprise' 'enthusiasm', 'relief', 'neutral', 'anger', 'worry', 'hate', 'happiness', love', 'fear', ‘empty' and ‘fun'.Features
Intensity of the emotions
It is also crucial to consider the contextual information in which the expression is occurring. Since the text has different emotions and our system predicts the possible emotions with their respective intensity score. As per the intensity score system predicts the emotions with high score on the text and plots the histogram.
User Input Text
User can enter their own text and the system predicts the emotion on that particular text.