Automate detection of different emotions from paragraphs and predict overall emotion.
Text-Emotion-Analysis is a project to develop rule-based and deep learning algorithms with an aim to first appropriately detect the different types of emotions contained in a collection of English sentences or a large paragraph and then accurately predict the overall emotion of the paragraph.
emotion_data.csv in which basic pre-processing of tweets in done (no lemmatization, no removal of stopwords).
This dataset is comprised of 55,774 tweets from Twitter with labelled emotions of five classes: Neutral, Happy, Sad, Love, Anger.
emotion_data_prep.csv in which more deep pre-processing of tweets in done (lemmatization, removal of stopwords, etc).
This dataset is comprised of 62,015 tweets from Twitter with labelled emotions of five classes: Neutral, Happy, Sad, Love, Anger.
The ML Algorithms used for prediction are listed as follows:
Model 1: Multinomial Naive Bayes Classifier - Accuracy 58.46%
Model 2: Linear SVM - Accuracy 62.00%
Model 3: Logistic Regression - Accuracy 62.47%
Model 1: Multinomial Naive Bayes Classifier - Accuracy 38.37%
Model 2: Linear SVM - Accuracy 38.49%
Model 3: Logistic Regression - Accuracy 40.13%