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

Year 2018, Volume: 6 Issue: 2, 69 - 77, 30.04.2018

Aytuğ Onan

https://doi.org/10.17694/bajece.419538

Cited By: 28

Abstract

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. 

Keywords

Machine learning, psychological feature sets, sentiment analysis, Twitter

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