Background:
Sentiment analysis is a contextual mining of text which determines viewpoint of
users with respect to some sentimental topics commonly present at social networking websites. Twitter is
one of the social sites where people express their opinion about any topic in the form of tweets. These tweets
can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment
analysis methods use manually extracted features for opinion classification. The manual feature extraction
process is a complicated task since it requires predefined sentiment lexicons. On the other hand,
deep learning methods automatically extract relevant features from data hence; they provide better performance
and richer representation competency than the traditional methods.
Objective:
The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the
computational cost.
Method:
To achieve the objective, a hybrid deep learning model, based on convolution neural network and
bi-directional long-short term memory neural network has been introduced.
Results:
The proposed sentiment classification method achieves the highest accuracy for the most of the datasets.
Further, from the statistical analysis efficacy of the proposed method has been validated.
Conclusion:
Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover,
performance can also be enhanced by tuning the hyper parameters of deep leaning models.
Supplementary material to "Surf3DNet1.0: A deep learning model for regional-scale 3D subsurface structure mapping"
