Deep Learning Based Long Short Term Memory Model for Emotions with Intensity Level Sentiment Classification for Twitter Texts
In last decades, social networking sites like Twitter and Facebook has provided a platform to express people opinions about product, politics, sports, entertainment and so on. At the same time, emotion and sentiment classification problem also attracted several researchers to analyze the state of mind of the people, which finds useful to improve the quality of the product or services. The recent developments of deep learning (DL) techniques started to be used for emotion and sentiment classification of social networking data. This paper presents an effective DL based Long Short Term Memory (LSTM) model for emotions with intensity level sentiment classification called LSTM-EISC for Twitter text. The proposed LSTM-EISC model follows a two-step procedure, namely prepocessing and classification. The LSTM model is applied to classify multiple intensities of Twitter data such as Fear, Anger, Joy, and Sadness. A series of experiments takes place on SEMEVAL2018 Task-1Emotion Intensity Ordinal Classification dataset to ensure the effective performance of the LSTM-EISC model. The simulation outcome pointed out the effective classification results of the LSTM-EISC model over the existing methods under several aspects.
Keywords: Deep Learning, Tokenization, Sentiment Analysis, Data Classification, Twitter.