Background:
For almost every domain, a tremendous degree of data is accessible in an
online and offline mode. Billions of users are daily posting their views or opinions by using different
online applications like WhatsApp, Facebook, Twitter, Blogs, Instagram etc.
Objective:
These reviews are constructive for the progress of the venture, civilization, state and even nation.
However, this momentous amount of information is useful only if it is collectively and effectively
mined.
Methodology:
Opinion mining is used to extract the thoughts, expression, emotions, critics, appraisal
from the data posted by different persons. It is one of the prevailing research techniques that coalesce
and employ the features from natural language processing. Here, an amalgamated approach has been
employed to mine online reviews.
Results:
To improve the results of genetic algorithm based opining mining patent, here, a hybrid genetic
algorithm and ontology based 3-tier natural language processing framework named GAO_NLP_OM has
been designed. First tier is used for preprocessing and corrosion of the sentences. Middle tier is composed
of genetic algorithm based searching module, ontology for English sentences, base words for the
review, complete set of English words with item and their features. Genetic algorithm is used to expedite
the polarity mining process. The last tier is liable for semantic, discourse and feature summarization.
Furthermore, the use of ontology assists in progressing more accurate opinion mining model.
Conclusion:
GAO_NLP_OM is supposed to improve the performance of genetic algorithm based opinion
mining patent. The amalgamation of genetic algorithm, ontology and natural language processing
seems to produce fast and more precise results. The proposed framework is able to mine simple as well
as compound sentences. However, affirmative preceded interrogative, hidden feature and mixed language
sentences still be a challenge for the proposed framework.
Optimized Strategic Planning of Future Norwegian Low-Voltage Networks with a Genetic Algorithm Applying Empirical Electric Vehicle Charging Data
