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- W4362570663 abstract "With the changing behavior of different types of social networking sites, such as Instagram, Twitter, SnapChat, etc. the amount of data shared by people, or users of a particular social site, is rapidly expanding. Every day, around million and billion pieces of data are uploaded. This is because a certain website has millions of users. These folks want to offer their thoughts and opinions on any topic they want. We hope to deduce the feelings behind these posts in this study. It is difficult to collect and analyze people’s reactions to purchasing a product, using public services, and so on. Sentiment analysis is a frequent debate preparation work that seeks to uncover the sentiments that underpin opinions in various texts. In recent years, sentiment analysis researchers have focused on assessing opinions on a variety of topics, including movies, commercial products, and everyday societal challenges. Twitter is one of the most popular micro-blogs where customers may express themselves. Opinion research using Twitter data has gotten a lot of attention in the previous decade. The two methodologies for assessing feelings from the text are first one is the knowledge base approach, and the second one is machine learning based approach. In this work, we use a Machine Learning based technique to evaluate tweets on electrical devices, such as smartphones and laptops. It is feasible to determine the influence of domain information on sentiment categorization by performing sentiment analysis in each domain. A novel feature vector for categorizing of tweets as positive or negative, as well as extracting people’s opinions on items has shown. Paper presents an analysis of popular methodologies for opinion mining, such as machine learning and lexicon based approaches, as well as assessment measures." @default.
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- W4362570663 date "2023-01-01" @default.
- W4362570663 modified "2023-10-13" @default.
- W4362570663 title "Evaluation of Tweet Sentiments Using NLP" @default.
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- W4362570663 doi "https://doi.org/10.1007/978-981-19-8094-7_17" @default.
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