Abstract
Use of social media for sharing the opinions about the products or the services by individuals or business organizations is becoming very common nowadays. Consumers are keen to share their views on certain products or commodities. This leads to the generation of large amount of unstructured social media data. Thus text data is being formed gradually in many areas like automated business, education, health care, show business and so on. Opinion mining, the sub field of text mining, deals with mining of review text and classifying the opinions or the sentiments of that text as positive or negative. The work in this paper develops a framework for opinion mining. It includes a novel feature selection method called Most Persistent Feature Selection (MPFS) for feature selection and a genetic algorithm (GA) based optimization technique for optimizing the feature set. MPFS method uses information gain of the features in the review documents. The feature set thus produced is optimized using GA technique to get the most effective feature set for sentiment classification. Then a Support Vector Machine (SVM) algorithm is used for classifying the sentiments of reviews expressed in text with the proposed feature selection and optimization method. The classifier models generated show the acceptable performance in terms of accuracy when compared with the other existing models.
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Sangam, S., Shinde, S. (2019). A Novel Feature Selection Method Based on Genetic Algorithm for Opinion Mining of Social Media Reviews. In: Minz, S., Karmakar, S., Kharb, L. (eds) Information, Communication and Computing Technology. ICICCT 2018. Communications in Computer and Information Science, vol 835. Springer, Singapore. https://doi.org/10.1007/978-981-13-5992-7_15
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DOI: https://doi.org/10.1007/978-981-13-5992-7_15
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