Twitter Sentiment Analysis

Machine Learning Algorithms Used

• KNN
• Naive Bayes
• Logistic Regression
• Multi-layer Perceptron
• Random Forest
• Support Vector Machine

Data Source

kaggle competitions download -c comp90049-2021s2-assignment3

Data Cleaning

• Check Null Values
• Convert to Lower Case
• Remove Tweet User Name
• Remove Special Characters
• Remove Stopwords (nltk.corpus.stopwords)
• Remove Punctuation
• Remove URL
• Remove Numbers
• Tokenization
• Remove Non-English Word (enchant)
• Stemming (nltk.stem.PorterStemmzer)
• Lemmatization (nltk.stem.WordNetLemmatizer)

Feature Extraction

• Bag of Word
• TF-IDF

Results

Different ML Algorithms Performance Table

Dataset	BoW (%)				TF-IDF (%)
Metrics	Accuracy	Precision	Recall	F1	Accuracy	Precision	Recall	F1
KNN	63.67	65.38	64.18	64.71	66.34	67.69	67.43	67.56
MNB	73.00	74.87	74.48	74.67	72.56	75.26	73.52	74.30
GNB	58.31	59.73	64.61	58.23	58.56	59.86	64.77	58.49
LR	74.14	75.57	75.90	75.73	74.26	75.61	76.08	75.84
MLP	69.07	70.94	70.76	70.85	69.19	71.11	71.10	71.04
RF	62.73	69.81	58.54	60.59	62.72	70.27	58.57	60.69
SVM	74.39	75.86	76.15	76.00	74.39	75.86	76.15	76.00