Abstract
The positive or negative polarity of an opinion on a product, event, or activity is not strengthen enough if the tweet of opinion exploring about the vivid aspects of the target objective. In this regard, many of the contributions in contemporary research has portrayed for sentiment analysis using supervised learning. However, the content on twitter, which is social media platform to share the opinion on anything, limits the size of each tweet. Hence, the users intend to express negative polarity of the opinion in their tweets. This trend has not consider much in contemporary contributions of sentiment analysis. In this context, the proposal of this manuscript portrayed “A New Method for Defining Scale to Estimate the Aspects Oriented Sentiment Polarity (SEAOSP) of the Tweets”. Experimental study evincing the significance of the proposal that scaled by comparing the performance of other contemporary model with the proposed model.
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The authors are thankful to the UGC, New Delhi for supporting this research work at School of Computer Sciences, KBCNMU, Jalgaon under the SAP DRS-II level.
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Sonawane, S.S., Kolhe, S.R. (2021). A New Method for Defining Scale to Estimate the Aspects Oriented Sentiment Polarity of the Tweets. In: Santosh, K.C., Gawali, B. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2020. Communications in Computer and Information Science, vol 1380. Springer, Singapore. https://doi.org/10.1007/978-981-16-0507-9_28
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