An Improvised Sentiment Analysis Model on Twitter Data Using Stochastic Gradient Descent (SGD) Optimization Algorithm in Stochastic Gate Neural Network (SGNN).
Autor: | Vidyashree KP; Department of Information Science and Engineering, Vidyavardhaka College of Engineering, Mysuru, India., Rajendra AB; Department of Information Science and Engineering, Vidyavardhaka College of Engineering, Mysuru, India. |
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Jazyk: | angličtina |
Zdroj: | SN computer science [SN Comput Sci] 2023; Vol. 4 (2), pp. 190. Date of Electronic Publication: 2023 Feb 02. |
DOI: | 10.1007/s42979-022-01607-x |
Abstrakt: | Sentiment analysis is one of the effective techniques for mining the opinion from shapeless data contains text like review of the products, review of the movie. Sentiment analysis is used as a key to gather response from consumers, reviews of brands, marketing analyses, and political campaigns. In the subject of natural processing, performing sentiment analysis using the data obtained from Twitter is considered as a new study in these days. The dataset is gathered using the Twitter API and the Twitter package. The analysis of Twitter data is a process which takes place automatically by text data analysis to determine the view of public on the specified topic. Here, an improvised sentimental analysis model is proposed to identify the polarity of the tweets such as positive, neutral and negative. In this paper, stochastic gradient descent (SGD) algorithm uses stochastic gradient neural network (SGNN) to categorize the sentiment analysis on basis of tweets provided by the Twitter users and the proposed stochastic gradient descent optimization Algorithm based on stochastic gradient neural network (SGDOA-SGNN) provides better performance when compared with the existing Forest-Whale Optimization Algorithm based on deep neural network F-WOA-DNN model. Competing Interests: Conflict of interestOn behalf of all authors, the corresponding author states that there is no conflict of interest. (© The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.) |
Databáze: | MEDLINE |
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