HANDS-ON-LAB

Emotion Classification from Tweets Deep Learning project

Problem Statement

Emotion detection from text is one of the challenging problems in Natural Language Processing. The reason is the unavailability of the labeled dataset and the multi-class nature of the problem. Humans have a variety of emotions, and it is difficult to collect enough records for each emotion and hence the problem of class imbalance arises. Here we have labeled data for emotion detection and the objective is to build an efficient model to detect emotion.

Dataset

Link to the data can be found here.

Tasks

  1. Remove ‘@xyz’ mentions and URL links from the content column. (Hint: use regex)

  2. Remove standard stopwords and punctuations from the text using the NLTK library. Then get the top frequent words in the text and analyze it to add a custom stoplist if a word has high frequency but not enough meaning to identify emotions.

  3. Convert the text to lowercase, apply lemmatization, and remove numbers from the content column.

  4. Create and visualize a word cloud from the final prepared text. 

  5. Create two datasets - one vectorized using count vectorizer and other using tf-idf vectorizer.

  6. Build the Naive Bayes algorithm on the two sets of final data and compare their performance.

FAQs

Q1. Why is emotion detection from text a challenging problem in Natural Language Processing?

Emotion detection is challenging due to the unavailability of labeled datasets and the multi-class nature of emotions, leading to class imbalance.

 

Q2. How can I preprocess the text data for emotion detection?

Use regular expressions to remove mentions and URLs, NLTK library to remove stopwords and punctuations, convert to lowercase, apply lemmatization, and remove numbers.

 

Q3. What methods can I use to compare the performance of emotion detection models?

Create two datasets with count vectorizer and tf-idf vectorizer, build the Naive Bayes algorithm on each, and compare their performance using appropriate metrics.