Yıl 2018, Cilt 6, Sayı 2, Sayfalar 1 - 9 2018-04-29

Sentiment Analysis on Twitter Based on Ensemble of Psychological and Linguistic Feature Sets

AYTUĞ ONAN [1]

111 106

With the advances in information and communication technologies, social media and microblogging platforms serve as an important source of information. In microblogging platforms, people can share their opinions, complaints, sentiments and attitudes towards topics, current issues and products. Sentiment analysis is an important research direction in natural language processing, which aims to identify the sentiment orientation of source materials. Twitter is a popular microblogging platform, where people all over the world can interact by user-generated text messages. Information obtained from Twitter can serve as an essential source for several applications, including event detection, news recommendation and crisis management. In sentiment classification, the identification of an appropriate feature subset plays an important role. LIWC (Linguistic Inquiry and Word Count) is an exploratory text analysis software to extract psycholinguistic features from text documents. In this paper, we present a psycholinguistic approach to sentiment analysis on Twitter. In this scheme, we utilized five main LIWC categories (namely, linguistic processes, psychological processes, personal concerns, spoken categories and punctuation) as feature sets. In the experimental analysis, five LIWC categories and their ensemble combinations are taken into consideration. To explore the predictive performance of different feature engineering schemes, four supervised learning algorithms (namely, Naïve Bayes, support vector machines, k-nearest neighbor algorithm and logistic regression) and three ensemble learning methods (namely, AdaBoost, Bagging and Random Subspace) are utilized. The experimental results indicate that ensemble feature sets yield higher predictive performance compared to the individual feature sets. 

Machine learning, psychological feature sets, sentiment analysis, Twitter
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Yazar: AYTUĞ ONAN

Bibtex @araştırma makalesi { bajece419538, journal = {Balkan Journal of Electrical and Computer Engineering}, issn = {2147-284X}, address = {Balkan Yayın}, year = {2018}, volume = {6}, pages = {1 - 9}, doi = {10.17694/bajece.419538}, title = {Sentiment Analysis on Twitter Based on Ensemble of Psychological and Linguistic Feature Sets}, key = {cite}, author = {ONAN, AYTUĞ} }
APA ONAN, A . (2018). Sentiment Analysis on Twitter Based on Ensemble of Psychological and Linguistic Feature Sets. Balkan Journal of Electrical and Computer Engineering, 6 (2), 1-9. DOI: 10.17694/bajece.419538
MLA ONAN, A . "Sentiment Analysis on Twitter Based on Ensemble of Psychological and Linguistic Feature Sets". Balkan Journal of Electrical and Computer Engineering 6 (2018): 1-9 <http://dergipark.gov.tr/bajece/issue/36835/419538>
Chicago ONAN, A . "Sentiment Analysis on Twitter Based on Ensemble of Psychological and Linguistic Feature Sets". Balkan Journal of Electrical and Computer Engineering 6 (2018): 1-9
RIS TY - JOUR T1 - Sentiment Analysis on Twitter Based on Ensemble of Psychological and Linguistic Feature Sets AU - AYTUĞ ONAN Y1 - 2018 PY - 2018 N1 - doi: 10.17694/bajece.419538 DO - 10.17694/bajece.419538 T2 - Balkan Journal of Electrical and Computer Engineering JF - Journal JO - JOR SP - 1 EP - 9 VL - 6 IS - 2 SN - 2147-284X- M3 - doi: 10.17694/bajece.419538 UR - http://dx.doi.org/10.17694/bajece.419538 Y2 - 2017 ER -
EndNote %0 Balkan Journal of Electrical and Computer Engineering Sentiment Analysis on Twitter Based on Ensemble of Psychological and Linguistic Feature Sets %A AYTUĞ ONAN %T Sentiment Analysis on Twitter Based on Ensemble of Psychological and Linguistic Feature Sets %D 2018 %J Balkan Journal of Electrical and Computer Engineering %P 2147-284X- %V 6 %N 2 %R doi: 10.17694/bajece.419538 %U 10.17694/bajece.419538
ISNAD ONAN, AYTUĞ . "Sentiment Analysis on Twitter Based on Ensemble of Psychological and Linguistic Feature Sets". Balkan Journal of Electrical and Computer Engineering 6 / 2 (Nisan 2018): 1-9. http://dx.doi.org/10.17694/bajece.419538