Sentiment Analysis Using Natural Language Processing

In this research, Our initial efforts on text based sentiment detection of tweets are presented in this publication. The motive of this experiment is to extract sentiment from tweets by using subjects that are present in them. Using methods from Natural Language Processing(NLP), it determines the sentiment that relevant to the specific topic. The subjective aspect classification, semantic connection, and polarity categorization are the three primary processes in our experiment that are used to classify sentiment. By establishing experiment uses sentiment lexical terms to determine the grammatical relationship between subject matter and sentiment lexicons. The suggested approach outperforms the existing text sentiment analysis technologies, according to experimental data, because tweets' structure differs from that of conventional text. The categorization of emotions in textual data is known as sentiment analysis, sometimes called opinion mining or emotion extraction. This approach has been widely used over time to pinpoint the feelings and ideas present in a particular textual information set. People mostly use Twitter as a means of social media to communicate their feelings on specific events. In this study, we gathered tweets for different occurrences, evaluated them using several automated learning methods, including LSTM, the Random Forest classifier, SVM, and Naïve Bays, and compared the outcomes.