Feature Selection in Genetic Algorithm Based Sentiment Classification Used Five Layered Artificial Neural Network for Cross Domain Opinion Mining

Abstract

In this paper, we present a review of Natural Language Processing (NLP) techniques for opinion mining. First, we introduce genetic algorithm for feature selection ANN used for classifier many researchers are proposing new ideas, models, applying machine learning algorithms, and more as a result of web mining and web usage mining. Internet use has expanded to practically all types of applications, including e-commerce. E-commerce enables consumers/customers to purchase things online, while web analytics enables website administrators to determine which products sell the most. In many e-commerce decision-making jobs, opinion mining is the key to analytics. A dataset of product reviews, such as books, DVDs, electronics, and kitchen appliances, is obtained. Genetic Algorithm is used to identify the features to perform opinion mining. The measures for measuring the performance of the proposed work are accuracy and F-measure. There is a comparison between domain-specific and domain-dependent words. The suggested work outperforms the existing work in terms of the chosen performance indicators, according to the findings.

Country : India

1 Dr. S.Gnanapriya2 D.Umanandhini

  1. Hod & Assistant Professor, Department of Computer Applications, Kovai Kalaimagal college of Arts and science, Narasipuram, Coimbatore-641109, Tamilnadu, India
  2. Hod & Assistant Professor, Department of Computer Science, Kovai Kalaimagal college of Arts and science, Narasipuram, Coimbatore-641109, Tamilnadu, India

IRJIET, Volume 7, Issue 2, February 2023 pp. 80-85

doi.org/10.47001/IRJIET/2023.702011

References

  1. Ali, Daehan Kwak, Pervez Khan, Shaker El-Sappagh, Amjad Ali, Sana Ullah, Kye Hyun Kim, Kyung-Sup Kwak, Transportation sentiment analysis using word embedding and ontology-based topic modeling, Knowledge-Based Systems, Volume 174, 2019, Pages 27-42.
  2. Chihli Hung, Shiuan-Jeng Chen, Word sense disambiguation based sentiment lexicons for sentiment classification, Knowledge-Based Systems, Volume 110, 2016, Pages 224-232.
  3. Sixing Wu, Yuanfan Xu, Fangzhao Wu, Zhigang Yuan, Yongfeng Huang, Xing Li, Aspect-based sentiment analysis via fusing multiple sources of textual knowledge, Knowledge-Based Systems, Volume 183, 2019, 104868. https://doi.org/10.1016/j.knosys.2019.104868.
  4. Pinlong Zhao, Linlin Hou, Ou Wu, Modeling sentiment dependencies with graph convolutional networks for aspect-level sentiment classification, Knowledge-Based Systems, Volume 193, 2020, 105443.
  5. María Lucía Barrón Estrada, Ramón Zatarain Cabada, Raúl Oramas Bustillos, Mario Graff, Opinion mining and emotion recognition applied to learning environments, Expert Systems with Applications, Volume 150, 2020, 113265.
  6. Aminu Da'u, Naomie Salim, Idris Rabiu, Akram Osman, Recommendation system exploiting aspect-based opinion mining with deep learning method, Information Sciences, Volume 512, 2020, Pages 1279-1292.
  7. Madhu Bala Myneni, Rohit Dandamudi, Harvesting railway passenger opinions on multi themes by using social graph clustering, Journal of Rail Transport Planning & Management, 2019, 100151.
  8. Aitor García-Pablos, Montse Cuadros, German Rigau, W2VLDA: Almost unsupervised system for Aspect Based Sentiment Analysis, Expert Systems with Applications, Volume 91, 2018, Pages 127-137.
  9. Nagendra Kumar, Rakshita Nagalla, Tanya Marwah, Manish Singh, Sentiment dynamics in social media news channels, Online Social Networks and Media, Volume 8, 2018, Pages 42-54.
  10. Vivian Lay Shan Lee, Keng Hoon Gan, Tien Ping Tan, Rosni Abdullah, Semi-supervised Learning for Sentiment Classification using Small Number of Labeled Data, Procedia Computer Science, Volume 161, 2019, Pages 577-584.
  11. Vinodhini Gopalakrishnan, Chandrasekaran Ramaswamy, Patient opinion mining to analyze drugs satisfaction using supervised learning, Journal of Applied Research and Technology, Volume 15, Issue 4, 2017, Pages 311-319.
  12. Mu-Yen Chen, Ting-Hsuan Chen, Modeling public mood and emotion: Blog and news sentiment and socio-economic phenomena, Future Generation Computer Systems, Volume 96, 2019, Pages 692-699.
  13. Feilong Tang, Luoyi Fu, Bin Yao, Wenchao Xu, Aspect based fine-grained sentiment analysis for online reviews, Information Sciences, Volume 488, 2019, Pages 190-204.