Comparison-of-Hybrid-Neural-Network-Methodologies-for-Sentiment-Emotion-Analysis
Twitter tweets play an important role in every organisation. This project is based on analysing the English tweets and categorizing the tweets based on the sentiment and emotions of the user. The literature survey conducted showed promising results of using hybrid methodologies for sentiment and emotion analysis. Four different hybrid methodologies have been used for analysing the tweets belonging to various categories. A combination of classification and regression approaches using different deep learning models such as Bidirectional LSTM, LSTM and Convolutional neural network (CNN) are implemented to perform sentiment and behaviour analysis of the tweets. A novel approach of combining Vader and NRC lexicon is used to generate the sentiment and emotion polarity and categories. The evaluation metrics such as accuracy, mean absolute error and mean square error are used to test the performance of the model. The business use cases for the models applied here can be to understand the opinion of customers towards their business to improve their service. Contradictory to the suggestions of Googleβs S/W ratio method, LSTM models performed better than using CNN models for categorical as well as regression problems.
