THE SENTIMENTAL ANALYSIS USING DEEP LEARNING MODELS

The tweets are brief and come in a steady stream. Emotions have a significant impact on feelings. People can express their ideas about anything and anything on social media. Public perception is divided into three categories: positive, negative, and neutral. In this study, Twitter hotel reviews are gathered and pre-processed before being analyzed using Python’s Tweepy package. Re-tweets, tags, URLs, hash tag symbols, and duplicate entries are all eliminated as part of a screening procedure to remove any discrepancies in the data. Using Python’s scikit-learn module, tweets are up-sampled and divided. Python turns textual data into vectors using the keras Tokenizer. Bi-sense Emoji Embedding (BSEE) is used to perform a sentimental analysis. Sentiment is categorized using Support Vector Machines (SVM) and Random Forest (RF),and LSTM (Long Short Term Memory) where compared based on accuracy, recall, F-measure, precision, time duration, and performance. It is clear that the proposed classifier produces better results.