Emotional Tweets Analysis on Social Media with Short Text Classification Using Various Machine Learning Techniques

Abstract

Online Social media are a huge source of regular communication since most people in the world today use these services to stay communicating with each other in their modern lives. Today’s research has been implemented on emotion recognition by message. The majority of the research uses a method of machine learning. In order to extract information from the textual text written by human beings, natural language processing (NLP) techniques were used. The emotion of humans may be expressed when reading or writing a message. Human beings are willing, since human life is filled with a variety of emotions, to feel various emotions. This analysis helps us to realize the use of text processing and text mining methods by social media researchers in order to classify key data themes. Our experiments presented that the two main social networks in the world are conducting text-based mining on Facebook and Twitter. In this proposed study, we categorized the human feelings such as joy, fear, love, anger, surprise, sadness and thankfulness and compared our results using various methods of machine learning.

A Sentiment Analysis Method for Teaching Evaluation Texts Using Attention Mechanism Combined with CNN-BLSTM Model

“…Similarly, the F1 value of the sentiment analysis results obtained by the proposed model and the models in [11,14,17] is shown in Figure 7.…”

“…A more accurate emotion recognition is achieved, but the recognition efficiency needs to be improved. Basha et al [14] proposed a machine learning text emotion recognition method. Using natural language processing technology, emotional information is extracted and recognized when reading or writing.…”

“…In order to verify the performance of the proposed model, a significance test experiment was designed. Using word frequency as a feature, the models in [11,14,17] and the proposed CNN-BLSTM-AT model are used for 10-fold cross-validation.…”

“…But it did not take into account the influence of different contexts on emotional orientation. Similarly, reference [14] uses machine learning for text emotion recognition.…”

“…e main reason is that the attention mechanism is introduced in the CNN-BLSTM model, which can better analyze various emotions that are not easy to recognize, such as disgust, insensitivity, and so on. Reference [11] uses existing sentiment dictionaries to combine web page information and manual annotations to realize sentiment analysis, and reference [14] performs sentiment classification based on machine learning. Neither of them considers the issue of multiple types of emotions in the teaching evaluation text dataset.…”