Abstract:
Nowadays, Twitter Sentiment analysis has
become popular as it helps the organization to
determine marketing strategy by providing public
opinions. Efficient techniques to collect a large amount
of Twitter stream data and extracting sentiment
information from collected raw data are essential
demand. Traditional sentiment classification
techniques do not perform well in Social Data.
Acquiring effective training data is a challenge
although learning based approaches are good for
Social Data Sentiment Classification. Manual Labeling
for training data is time and labor consuming. In this
paper, Sentiment Analysis System for Twitter data is
proposed with five modules: Data Collection,
Preprocessing, Class Labeling, Classification Model
Development and Sentiment Classification. The
Sentiment Classification is implemented by combining
lexicon and Supervised learning-based approaches. In
this system, lexicon-based classifier is applied to label
the class and suitable learning-based classifier is
chosen for classification. Emoticon and slang words
are considered for classification. To select suitable
classifier, three different classification algorithms are
evaluated. The performance evaluation shows that
Naïve Bayes classifier is better and the proposed
system can achieved the promising accuracy.
