Sentiment Analysis in Twitter Based on Knowledge Graph and Deep Learning Classification

Abstract

The traditional way to address the problem of sentiment classification is based on machine learning techniques; however, these models are not able to grasp all the richness of the text that comes from different social media, personal web pages, blogs, etc., ignoring the semantic of the text. Knowledge graphs give a way to extract structured knowledge from images and texts in order to facilitate their semantic analysis. This work proposes a new hybrid approach for Sentiment Analysis based on Knowledge Graphs and Deep Learning techniques to identify the sentiment polarity (positive or negative) in short documents, such as posts on Twitter. In this proposal, tweets are represented as graphs; then, graph similarity metrics and a Deep Learning classification algorithm are applied to produce sentiment predictions. This approach facilitates the traceability and interpretability of the classification results, thanks to the integration of the Local Interpretable Model-agnostic Explanations (LIME) model at the end of the pipeline. LIME allows raising trust in predictive models, since the model is not a black box anymore. Uncovering the black box allows understanding and interpreting how the network could distinguish between sentiment polarities. Each phase of the proposed approach conformed by pre-processing, graph construction, dimensionality reduction, graph similarity, sentiment prediction, and interpretability steps is described. The proposal is compared with character n-gram embeddings-based Deep Learning models to perform Sentiment Analysis. Results show that the proposal is able to outperforms classical n-gram models, with a recall up to 89% and F1-score of 88%.

A Novel Cascade Model for End-to-End Aspect-Based Social Comment Sentiment Analysis

“…Additionally, experiments prove that the proposed approach provides better F1-scores of 68.13% , 62.34% , 56.40% and 50.05% for SemEval 2014 [31], 2015 [32], 2016 [33] and the Twitter dataset [34], respectively. To some extent, these experiments prove that our proposed model outperforms on real-life datasets compared to many previously widely used methods [14,17,20].…”

“…Datasets. We evaluate our proposed CasNSA on four widely benchmark datasets, including SemEval challenges 2014 [31], 2015 [32], 2016 [33], and the Twitter dataset [34,43]. These benchmark statistics are summarized in Table 3.…”

Análisis de sentimientos en Twitter

“…Entonces, la única ventaja intuitiva que esperaríamos con modelos TL es poder entrenar modelos del lenguaje en unos pocos días en lugar de meses. Esperamos en trabajos futuros comparar resultados con otros modelos como TLs, e incluso con Grafos del Conocimiento (Lovera et al, 2021), Knowledge Graphs en inglés, entre otros. Estos otros métodos también ofrecen comportamientos de aprendizaje alternos y pueden potencialmente ser beneficiosos para el análisis de texto, en particular de sentimientos.…”

Cluster-Based Knowledge Graph and Entity-Relation Representation on Tourism Economical Sentiments

“…Knowledge graphs are becoming critical in expressing knowledge retrieved through natural language processing and machine learning techniques. Knowledge graphs that are inputted in advanced and evolved machine learning models help to make more accurate and precise predictions [7,8].…”