Efficient Sentimental Analysis using Hybrid Deep Transfer Learning Neural Network

Efficient Sentimental Analysis using Hybrid Deep Transfer Learning Neural Network

© 2022 by IJETT Journal
Volume-70 Issue-10
Year of Publication : 2022
Authors : K. Kavitha, Suneetha Chittineni
DOI : 10.14445/22315381/IJETT-V70I10P216

How to Cite?

K. Kavitha, Suneetha Chittineni, "Efficient Sentimental Analysis using Hybrid Deep Transfer Learning Neural Network," International Journal of Engineering Trends and Technology, vol. 70, no. 10, pp. 155-165, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I10P216

Sentiment analysis has become a famous exploration theme for recovering important data from different online conditions. Most existing sentiment examines depend on the managed realization that supervised learning requires an adequate measure of marked information. Be that as it may, sentiment analysis frequently faces deficient named information, practically speaking, as it is pricey and tedious to mark the enormous measure of information. To deal with the lack of beginning named information, this article presented a deep and effective method for sentiment analysis through a hybrid deep-learning neural network. Unstructured data is first converted into a structured format and then pre-processed through tokenization, stop word removal, and the weighting factor. According to the retrieved sentiment features, such as emotion, personality traits, and demographic features within the pre-processed data, a deep learning neural network classifies the data for sentiment analysis. SGOA (Support-based Grasshopper Optimization Algorithm) is used to tune the weight parameter of each layer of the suggested Hybrid Deep Transfer Learning Neural Network (HDNN). At last, performance compared with these existing models, the proposed model achieves the highest analysis value.

Sentiment Analysis, HDNN, Tokenization, Stop Word Removal, Weighting Factor, Support Value, Grasshopper Optimisation Algorithm.

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