Automate Detection of Different Emotions from Textual Comments And Feedback.
| Internship Project Title | TCSiON RIO-45 |
| Project Title | Automate detection of different emotions from textual comments and feedback. |
| Name of the Industry Mentor | Mr. Debashish Roy |
| Name of the Institute | Rajkiya Engineering College,Banda |
Automated detection of different emotions refers to the use of language processing and text analysis to identify the different emotions in a comment or feedback. I have created a list of different types of human emotions that belong to a particular word from the dictionary. After cleansing of textual comments, the algorithm searches the keyword of textual comments that matches the emotion list. The algorithm counts the appearance of different emotions and how many times in comments a particular emotion appears. The output will be the histogram representation of different emotions.
Solution Approach: Approach can be grouped into three main categories.
•Tokenization
•Removing stop words
•Keyword Spotting
Tokenization: Different textual comments are broken into tokens.it is a convenient approach for the detection of emotions.
Removing stop words: Different stop words are removed from the tokenized list so that the words that belong to a particular emotion remain only.
Keyword Spotting: After the cleansing of data, the algorithm searches the keyword of textual comments that match the emotion list.
Methodology
Step 1: Choose comments or feedback.
Step 2:Cleansing of comments or feedback.
Step 3:Collect different types of emotions from the English dictionary.
Step 4:Search the keywords of emotion from the comment or feedback and match from the dictionary emotions.
Step 5: Count the appearance of each emotion in the textual comments.
Step 6:Pass key and value to make the graph using matplotlib.
Outcome: One can find the occurrence of different emotions in textual comments or feedback and easily figure out which is the dominant emotion in the textual comment.
Quickstart:
•The application can be run by simply executing main.py