Sentiment analysis of malayalam tweets using machine learning techniques
Author(s) -
S Soumya,
Pramod K.V.
Publication year - 2020
Publication title -
ict express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.733
H-Index - 22
ISSN - 2405-9595
DOI - 10.1016/j.icte.2020.04.003
Subject(s) - artificial intelligence , naive bayes classifier , tf–idf , support vector machine , malayalam , computer science , random forest , sentiment analysis , natural language processing , machine learning , negation , classifier (uml) , feature (linguistics) , bigram , term (time) , pattern recognition (psychology) , trigram , linguistics , philosophy , physics , quantum mechanics , programming language
Sentiment Analysis of Malayalam Tweets using Machine Learning techniques is done in this paper. The tweets are classified into positive and negative using different machine learning techniques such as Naive Bayes (NB), Support Vector Machine (SVM) and Random Forest (RF). The different features like Bag of Words (BOW), Term Frequency vs. Inverse Document Frequency (TF − IDF), Unigram with Sentiwordnet, and Unigram with Sentiwordnet including negation words are considered for feature vector formation of input data set. The Random Forest classifier shows higher accuracy while considering Unigram with Sentiwordnet including negation words as a feature.
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