Identification of Positive and Negative Tweets
Kunti Dongare, Neha S. Mahajan, Gayatri Takawale, Akshada Thange, Prof. Runal. P. Pawar · International Journal for Research in Applied Science and Engineering Technology · 2023
Abstract: With the development of the Internet, people can obtain and share information almost instantly from a wide array of sources, for example, online news outlets and fast-growing social networks. Over the past few decades, the explosive growth of textual data far outpaces human beings’ speed of understanding its content. Indeed, we have seen the emergence of new types of textual data that reflect social interactions in online settings. The production of this socially-generated content is accelerated by the wide adoption of social media sites, such as Facebook, Twitter, Yahoo! Answers, and Reddit. Text summarization helps in reducing the size of a text while preserving its information content. Text Summarization can be derived as shortening the source text into a version that it’s information content and overall meaning is preserved. It is very difficult for human beings to manually summarize large documents of text. Sentiment analysis on the other hand is the process of computationally identifying and categorizing opinions expressed in text to clarify someone’s’ attitude towards a topic is positive or else negative or even neutral. In this paper we will discuss the use Extractive summarization for the text summarization followed by SVM-algorithm for sentiment analysis.