Aims:
The proposed research work is on an evolutionary enhanced method for sentiment or
emotion classification on unstructured review text in the big data field. The sentiment analysis plays a
vital role for current generation of people for extracting valid decision points about any aspect such as
movie ratings, education institute or politics ratings, etc. The proposed hybrid approach combined the
optimal feature selection using Particle Swarm Optimization (PSO) and sentiment classification
through Support Vector Machine (SVM). The current approach performance is evaluated with statistical
measures, such as precision, recall, sensitivity, specificity, and was compared with the existing approaches.
The earlier authors have achieved an accuracy of sentiment classifier in the English text up
to 94% as of now. In the proposed scheme, an average accuracy of sentiment classifier on
distinguishing datasets outperformed as 99% by tuning various parameters of SVM, such as constant c
value and kernel gamma value in association with PSO optimization technique. The proposed method
utilized three datasets, such as airline sentiment data, weather, and global warming datasets, that are
publically available. The current experiment produced results that are trained and tested based on 10-
Fold Cross-Validations (FCV) and confusion matrix for predicting sentiment classifier accuracy.
Background:
The sentiment analysis plays a vital role for current generation people for extracting valid decisions
about any aspect such as movie rating, education institute or even politics ratings, etc. Sentiment
Analysis (SA) or opinion mining has become fascinated scientifically as a research domain for the present
environment. The key area is sentiment classification on semi-structured or unstructured data in distinguish
languages, which has become a major research aspect. User-Generated Content [UGC] from distinguishing
sources has been hiked significantly with rapid growth in a web environment. The huge user-generated data
over social media provides substantial value for discovering hidden knowledge or correlations, patterns, and
trends or sentiment extraction about any specific entity. SA is a computational analysis to determine the
actual opinion of an entity which is expressed in terms of text. SA is also called as computation of emotional
polarity expressed over social media as natural text in miscellaneous languages. Usually, the automatic superlative
sentiment classifier model depends on feature selection and classification algorithms.
Methods:
The proposed work used Support vector machine as classification technique and particle
swarm optimization technique as feature selection purpose. In this methodology, we tune various
permutations and combination parameters in order to obtain expected desired results with kernel and
without kernel technique for sentiment classification on three datasets, including airline, global warming,
weather sentiment datasets, that are freely hosted for research practices.
Results:
In the proposed scheme, The proposed method has outperformed with 99.2% of average accuracy
to classify the sentiment on different datasets, among other machine learning techniques. The attained
high accuracy in classifying sentiment or opinion about review text proves superior effectiveness over
existing sentiment classifiers. The current experiment produced results that are trained and tested based
on 10- Fold Cross-Validations (FCV) and confusion matrix for predicting sentiment classifier accuracy.
Conclusion:
The objective of the research issue sentiment classifier accuracy has been hiked with the
help of Kernel-based Support Vector Machine (SVM) based on parameter optimization. The optimal
feature selection to classify sentiment or opinion towards review documents has been determined with
the help of a particle swarm optimization approach. The proposed method utilized three datasets to
simulate the results, such as airline sentiment data, weather sentiment data, and global warming data
that are freely available datasets.
Feature selection with acquisition cost for optimizing sensor system design
