Abstract
Nowadays, social media platforms, blogs, and e-commerce are commonly use to express opinion on politics, movies, products, education respectively; for election forecasting, business boosting and improvement of teaching and learning. As a result, data generation becomes easier; producing big data which requires appropriate techniques and tools to analyse easily, accurately and timely. Thus, making sentiment analysis very demanding research area. This study will investigate on what basis (sentiment classification level) or area of application (data source) do supervised machine learning approaches particularly Support Vector Machine (SVM), Naïve Bayes, and Maximum Entropy algorithms, and other technique-lexicon-based approach give the best result in sentiment analysis. Based on the review of the literature there is a contradiction on the point that SVM generated the best result in analyzing student sentiment on document level. This study also discovers that sentiment analysis differs from system to system based on polarity (types of the classes to predict: positive or negative, subjective or objective), different levels of classification (sentence, phrase, or document level) and language that is processed. This research produces a taxonomy which serves as a guide for the choice of techniques in sentiment analysis. The taxonomy explores the sentiment classification levels and data preprocessing stages. It also explores that sentiment analysis techniques were organised in to three (3) groups; Machine learning, Lexicon and hybrid or combination. The machine learning techniques were sub-grouped in to two (2) namely; supervised and unsupervised. The supervised were organized in to two (2): Classification and Regression. un-supervised machine learning techniques includes clustering and association. The clustering technique consist of k-means. Decision tree which is a classification based under supervised type of machine learning technique consist of random forest,(Akinkunmi, 2019) while the ruled-based classifiers consist of confidence criterion and support criterion. The commonly used tools are Weka, Python compiler, and R programming tool.
Highlights
In a study by Bose et, al (2018) sentiment analysis can be expressed as a tool used to analyze opinions or moods on a specific matter-services, products or subject written in text or extracted from social media platforms, blog post, comments or web reviews and so on
The author further reported that, diverse techniques machine learning algorithm have been utilized in sentiment analysis and a couple has demonstrated to give the efficient and accurate result, these are Support Vector Machines (SVM), Naïve Bayes and Max Entropy (MaxEnt)
The decision tree technique consist of random forest technique (Akinkunmi, 2019) while the ruled-based classifiers consist of confidence criterion and support criterion as reported by (Bose et al, 2018)
Summary
In a study by Bose et, al (2018) sentiment analysis can be expressed as a tool used to analyze opinions or moods on a specific matter-services, products or subject written in text or extracted from social media platforms, blog post, comments or web reviews and so on. Sentiments analysis deals classification of opinions or based on its polarity-label or bearing of the opinion according to the three(3) classes: negative, positive or neutral (Alkubaisi et al 2018). Lexicons perform better than machine learning approaches on large data in Urdu Language based on Accuracy, Precision, Recall, F-measure and even economy of time and efforts used (Neelam et al 2018). The author had identified Machine Learning approach as the most effective and reliable in the field of sentiment analysis and opinion mining for classification purposes. How can the survey promote choice of algorithm and analytical and data collection tool suitable in sentiment analysis? Contribution of this research will produce a taxonomy of sentiment analysis techniques as well as guide for the choice of techniques and analytical and data collection tools in “Sentiment Analysis Techniques and Application-Survey and Taxonomy” The survey will answer the following question: Can taxonomy of all the sentiment analysis techniques enhances future survey in the same area? How can the survey promote choice of algorithm and analytical and data collection tool suitable in sentiment analysis? Contribution of this research will produce a taxonomy of sentiment analysis techniques as well as guide for the choice of techniques and analytical and data collection tools in “Sentiment Analysis Techniques and Application-Survey and Taxonomy”
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