Modified term frequency-inverse document frequency based deep hybrid framework for sentiment analysis.

Abstract

Sentiment Analysis is a highly crucial subfield in Natural Language Processing that attempts to extract the public sentiment from the accessible user opinions. This paper proposes a hybridized neural network based sentiment analysis framework using a modified term frequency-inverse document frequency approach. After preprocessing of data, the basic term frequency-inverse document frequency scheme is improved by introducing a non-linear global weighting factor. This improved scheme is combined with the k-best selection method to vectorize textual features. Next, the pre-trained embedding technique is employed for the mathematical representation of the textual features to process them efficiently by the Deep Learning methodologies. The embedded features are then passed to the deep neural network, consisting of Convolutional Neural Network and Long Short Term Memory. Convolutional Neural Networks can build hierarchical representations for capturing locally embedded features within the feature space, and Long Short Term Memory tries to recall useful historical information for sentiment polarization. This deep neural network finally provides the sentiment label. The proposed model is compared with different state-of-the-art baseline models in terms of various performance metrics using several datasets to demonstrate its efficacy.