Sentiment Analysis on Twitter Data using Modified Neuro Fuzzy Algorithm
Keywords:
Twitter data, MPCA, K-fold validation, MFNA, kaggleAbstract
The utilization of Social Media (SM) has been widely adopted by individuals as a convenient and formal means of communication. Individuals engage in the act of composing or disseminating textual content, as well as appending visual media such as images and videos, on various social networking platforms such as Twitter, Facebook, and other analogous digital platforms. Sentiment analysis (SA), also known as Opinion Mining (OM), is a prevalent task in dialogue preparation that seeks to uncover the underlying sentiments expressed in texts pertaining to diverse topics. The collection of data from social media platforms can be effectively leveraged to address various objectives, including the marketplace prediction, product suggestion, and analysis of reviewer sentiment. Managing unstructured data found on social media poses a significant challenge. Deep learning algorithms are a suitable solution for addressing the challenges associated with handling this type of data. Therefore, this study aims to conduct SA on a dataset collected from Twitter. In the proposed study, the initial step involves preprocessing the input Twitter data. Following the preprocessing stage, the words are organized in a structured format utilizing HDFS (Hadoop Distributed File System). The process of Map Reducing is then applied to eliminate duplicate words and establish the structured format. The features are derived from the Proposed Modified Principal Component Analysis (MPCA) method. In the final stage, the features are classified utilizing the Modified Fuzzy Neural Algorithm (MFNA) that has been proposed. The simulation results of the proposed method demonstrate superior performance in comparison to existing methods. Ultimately, the outcome was evaluated through the utilization of the K Fold Cross Validation technique. The aforementioned procedures are implemented in twitter data set publicly available in the Kaggle. The highest level of accuracy achieved is 96.984%, which is correlated with the sentiments of three classes positive, negative and neutral.
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