Abstract
The current decade has witnessed the remarkable developments in the field of artificial intelligence, and the revolution of deep learning has transformed the whole artificial intelligence industry. Eventually, deep learning techniques have become essential components of any model in today’s computational world. Nevertheless, ensemble learning techniques promise a high degree of automation with generalized rule extraction for both text and sentiment classification tasks. This paper aims designed and implemented optimized feature matrix using ensemble learning used for sentiment classification and its applications.
Highlights
The explosive growth of opinion content generated through commercial websites and recent advances in data analytics together have placed new challenges and opportunities [1]
Sentiment analysis methods can be generally divided into two categories, machine learning and lexicon-based methods
Yang et al [1] proposed a new mood analysis modelSLCABG, which is based on the mood lexicon and combines the convolutional neural network (CNN) and the attention-based bidirectional recurrent unit (BiGRU)
Summary
The explosive growth of opinion content generated through commercial websites and recent advances in data analytics together have placed new challenges and opportunities [1]. It is hard to explore the correlation among opinion sentences due to the diversity of linguistic issues and makes the process of sentiment analysis still more challenging [5]. To address these challenges, real-time sentiment analysis systems need to be developed for processing large volumetric opinion data in a reasonable amount of time. Sentiment analysis methods can be generally divided into two categories, machine learning and lexicon-based methods. The former uses machine learning techniques for sentiment polarity classification.
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