Emotional Text Classification Using TF-IDF (Term Frequency-Inverse Document Frequency) And LSTM (Long Short-Term Memory)

Humans in carrying out communication
activities can express their feelings either verbally or nonverbally. Verbal communication can be in the form of oral
or written communication. A person's feelings or emotions
can usually be seen by their behavior, tone of voice, and
expression. Not everyone can see emotion only through
writing, whether in the form of words, sentences, or
paragraphs. Therefore, a classification system is needed to
help someone determine the emotions contained in a piece
of writing. The novelty of this study is a development of
previous research using a similar method, namely LSTM
but improved on the word weighting process using the TFIDF method as a further process of LSTM classification.
The method proposed in this research is called Natural
Language Processing (NLP). The purpose of this study was
to compare the classification method with the LSTM (Long
Short-Term Memory) model by adding the word weighting
TF-IDF (Term Frequency–Inverse Document Frequency)
and the LinearSVC model, as well to increase accuracy in
determining an emotion (sadness, anger, fear, love, joy,
and surprise) contained in the text. The dataset used is
18000, which is divided into 16000 training data and 2000
test data with 6 classifications of emotion classes, namely
sadness, anger, fear, love, joy, and surprise. The results of
the classification accuracy of emotions using the LSTM
method yielded a 97.50% accuracy while using the
LinearSVC method resulted in an accuracy value of 89%.