Abstract
Sentiment analysis has been one of the most active research areas in the past decade due to its vast applications. Sentiment quantification, a new research problem in this field, extends sentiment analysis from individual documents to an aggregated collection of documents. Sentiment analysis has been widely researched, but sentiment quantification has drawn less attention despite offering a greater potential to enhance current business intelligence systems. In this research, to perform sentiment quantification, a framework based on feature engineering is proposed to exploit diverse feature sets such as sentiment, content, and part of speech, as well as deep features including word2vec and GloVe. Different machine learning algorithms, including conventional, ensemble learners, and deep learning approaches, have been investigated on standard datasets of SemEval2016, SemEval2017, STS-Gold, and Sanders. The empirical-based results reveal the effectiveness of the proposed feature sets in the process of sentiment quantification when applied to machine learning algorithms. The results also reveal that the ensemble-based algorithm AdaBoost outperforms other conventional machine learning algorithms using a combination of proposed feature sets. The deep learning algorithm RNN, on the other hand, shows optimal results using word embedding-based features. This research has the potential to help diverse applications of sentiment quantification, including polling, trend analysis, automatic summarization, and rumor or fake news detection.
Highlights
The social web has changed the way people communicate
There is room to improve the accuracy for sentiment-based quantification
Sentiment quantification is not addressed with feature-based approaches to achieve the desired accuracy
Summary
The social web has changed the way people communicate. The emergence of social media channels has resulted in the rapid creation of textual content. The rapid growth of content has sentiment information, which offers the potential for researchers to obtain people’s opinion through social media about entities including business, academia, products, marketing, etc. Sentiment analysis is an active research area that classifies opinions in negative, positive, and neutral texts. It finds the grade of polarity (high, moderate, and mild). Document-level sentiment analysis creates groups of documents and classifies the target documents into the required set of classes. The target documents are classified as positive or negative, while for tertiary classification the required classes include positive, negative, and neutral. Document-level sentiment analysis does not consider diverse factors for analysis. Document-level and sentence-level sentiment analysis do not give a clear understanding of the polarity of the text. Sentiment analysis has various research areas, including subjectivity analysis, sentiment polarity detection [3], sentiment quantification, etc. [4]
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