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• W4313176508 abstract "Sentimental Analysis or Opinion Mining is one of the major areas of NLP. SA is discourse mining of text that identifies and extracts subjective data in supply material, and serves businesses to grasp the social sentiment of their complete product or service. However, sentiments of social media streams perform only normal intent classification setting limit to its extensive usage. Aim is to use this sentiment scrutiny at its best and also reducing the cost and time factors. Because SA is widely use by big scale companies but small-scale industries are still far away from it as either it is too time consuming or not affordable. So here with a little change in self-attention mechanism which reduces the time of processing. Apparently in last few years this field has grabbed a lot of attention resulting in advancement of technologies like machine learning, deep learning neural network algorithms, whose contributions are giving more worthier outputs. But neural networks are good at learning automatically through semantic representations of high dimensional data without a well framed engineering design. That means they have auto learning ability but not auto focusing ability to identify the important sentiments objects in the statement, this is where attention mechanism is applied. Now as stated motive is that small scale businesses can also afford on aspects of cost and time this self-attention mechanism is combined with POS tagging." @default.
• W4313176508 created "2023-01-06" @default.
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• W4313176508 date "2022-09-08" @default.
• W4313176508 modified "2023-09-27" @default.
• W4313176508 title "Categorizing Data for Sentimental Analysis by Auto-focusing mechanism using Natural Language Processing" @default.
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• W4313176508 doi "https://doi.org/10.1109/iccsea54677.2022.9936363" @default.
• W4313176508 hasPublicationYear "2022" @default.
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