%0 Journal Article
%T Sentiment Analysis on Social Media for Albanian Language
%A Roland Vasili
%A Endri Xhina
%A Ilia Ninka
%A Dhori Terpo
%J Open Access Library Journal
%V 8
%N 6
%P 1-31
%@ 2333-9721
%D 2021
%I Open Access Library
%R 10.4236/oalib.1107514
%X The recent advances in technology and particularly, the rising prominence of social media platforms have made it possible to express our emotions through electronic means, which have led to the creation of large collections of unstructured textual documents. These collections can be saved and potentially studied with many modern technologies like Text Mining, Machine Learning and Natural Language Processing to obtain new knowledge from them. Sentiment Analysis is a field of Natural Language Processing that focuses on extracting sentiment from text. Moreover, as a Text Mining technique expresses the ability to track the subjective opinion of a text produced by an entity. The purpose of this paper is to test and review different approaches in Sentiment Analysis for messages in the Albanian language found on Twitter. Additionally, we compare the results among different methods and note the challenges that arise while finally we suggest future directions for further research. This paper¡¯s research was conducted as follows: the data was pre-processed, before being converted from text to vector representation using a range of feature extraction techniques such as Bag-of-Words, TF-IDF, Word2Vec, and Glove. We study the performance of sentiment classification techniques from three main approaches: traditional machine learning, lexicon-based and deep learning approach. For model evaluation, since they were trained in unbalanced data, we used not only classical evaluation criteria such as Accuracy, Specificity, Precision, and Recall but more appropriate criteria such as F-measure, Balanced Accuracy, and Matthews Correlation Coefficient (MCC). According to all these criteria, our experiments revealed that LSTM based RNN with Glove as a feature extraction technique provides the best results with F-score = 87.8%, followed by Logistic Regression.
%K Sentiment Analysis
%K Sentiment Lexicon
%K Text Mining
%K Machine Learning
%K Twitter
%U http://www.oalib.com/paper/6756207