Machine Learning Sentiment Prediction based on Hybrid Document Representation
This work addresses sentiment analysis for commercial applications by improving text representation, but it is incremental as it combines existing methods without a major breakthrough.
The authors tackled sentiment analysis by proposing a hybrid vectorization approach combining weighted Word2Vec, Bag-of-Words, and lexicon-based sentiment values, achieving higher classification accuracy than individual components and comparable results to state-of-the-art methods on a standard dataset.
Automated sentiment analysis and opinion mining is a complex process concerning the extraction of useful subjective information from text. The explosion of user generated content on the Web, especially the fact that millions of users, on a daily basis, express their opinions on products and services to blogs, wikis, social networks, message boards, etc., render the reliable, automated export of sentiments and opinions from unstructured text crucial for several commercial applications. In this paper, we present a novel hybrid vectorization approach for textual resources that combines a weighted variant of the popular Word2Vec representation (based on Term Frequency-Inverse Document Frequency) representation and with a Bag- of-Words representation and a vector of lexicon-based sentiment values. The proposed text representation approach is assessed through the application of several machine learning classification algorithms on a dataset that is used extensively in literature for sentiment detection. The classification accuracy derived through the proposed hybrid vectorization approach is higher than when its individual components are used for text represenation, and comparable with state-of-the-art sentiment detection methodologies.