Neural Network Model to Classify a Tweet According to Its Sentiment

This research presents a recurrent neural network model that analyzes tweets and classifies them according to three categories: “positive”, “neutral” and “negative”, in order to identify supporting phrases in the semantic context of the topic under analysis. To do this, the Tweet Sentiment Extraction dataset and the LSTM neural model are used to train the network and evaluate, through network performance measurements and expert analysis, the precision and sensitivity of the model. The results allow us to classify the sentiment behind a specific tweet with 86.78% accuracy, which shows that the network has a high level of precision, in relation to the expert’s assessment and comparing it with other works that have analyzed the sentiment. Dataset using Sentiment Analysis, Topic Modeling among other techniques. Additionally, a web dashboard is presented, which integrates the model analysis flow and allows the visualization of the results of the tweet classification. In this way, the work offers a tool that can be used to classify the large amount of information on Twitter according to its polarity, recognizing and classifying patterns according to the semantic meaning of the terms. The applications of the model are many, for example, for understanding the characteristics of the personality of the public on social networks, the perception of customers in relation to products, services; which allows us to recognize opportunities to adjust marketing strategies, which benefit both clients and companies in general.