A Knowledge-Based Deep Learning Architecture for Aspect-Based Sentiment Analysis

Abstract

The task of sentiment analysis tries to predict the affective state of a document by examining its content and metadata through the application of machine learning techniques. Recent advances in the field consider sentiment to be a multi-dimensional quantity that pertains to different interpretations (or aspects), rather than a single one. Based on earlier research, the current work examines the said task in the framework of a larger architecture that crawls documents from various online sources. Subsequently, the collected data are pre-processed, in order to extract useful features that assist the machine learning algorithms in the sentiment analysis task. More specifically, the words that comprise each text are mapped to a neural embedding space and are provided to a hybrid, bi-directional long short-term memory network, coupled with convolutional layers and an attention mechanism that outputs the final textual features. Additionally, a number of document metadata are extracted, including the number of a document’s repetitions in the collected corpus (i.e. number of reposts/retweets), the frequency and type of emoji ideograms and the presence of keywords, either extracted automatically or assigned manually, in the form of hashtags. The novelty of the proposed approach lies in the semantic annotation of the retrieved keywords, since an ontology-based knowledge management system is queried, with the purpose of retrieving the classes the aforementioned keywords belong to. Finally, all features are provided to a fully connected, multi-layered, feed-forward artificial neural network that performs the analysis task. The overall architecture is compared, on a manually collected corpus of documents, with two other state-of-the-art approaches, achieving optimal results in identifying negative sentiment, which is of particular interest to certain parties (like for example, companies) that are interested in measuring their online reputation.

Deep Learning Using Context Vectors to Identify Implicit Aspects

“…In addition, the Rule-based and Knowledge-based methods are also interested in using and solving many context-related problems in sentiment analysis [22]. The authors in [23] present a multi-level approach to identify implicit aspects thanks to techniques of co-occurrence and similarity.…”

Mixed artificial reasoning, closer to human?

“…On the contrary, Feature extraction change the initial variables using the prior knowledge of the ontology for get relevant features (Kumar et al, 2020;Radovanovic et al, 2019;Evert et al, 2019;Agarwal et al, 2015;Radinsky et al, 2012;Greenbaum et al, 2019;Liu et al, 2021;Rinaldi et al, 2021;Castillo et al, 2008;Yilmaz, 2017;Hsieh et al, 2013;Rajput and Haider, 2011;Manuja and Garg, 2015;Ahani et al, 2021;Akila et al, 2021;Deepak et al, 2022;Messaoudi et al, 2021;Nayak et al, 2021;Pérez-Pérez et al, 2021;Zhao et al, 2021;. In semantic embedding, always in training data step, raw data are both refined by semantic knowledge and transformed into vectors to be exploited by neural networks (Chen et al, 2021;Ren et al, 2020;Qiu et al, 2019;Ali et al, 2019;Zhang et al, 2019;Makni and Hendler, 2019;Benarab et al, 2019;Moussallem et al, 2019;Gaur et al, 2019;Jang et al, 2018;Hassanzadeh et al, 2020;Ali et al, 2021;Amador-Domínguez et al, 2021;Alexandridis et al, 2021;Niu et al, 2022), SVM (Mabrouk et al, 2020) or XGBosst (Zhang et al, 2021). The oldest paper in this SLR that uses this technique is from 2018, we can therefore assume that research in this field is recent.…”

“…Table 8 presents machine learning algorithms of each informed machine learning category. Articles were mainly published after 2017, and a great part of them concern neural networks (Hassanzadeh et al, 2020;Gaur et al, 2019;Ali et al, 2019;Jang et al, 2018;Zhang et al, 2019;Ali et al, 2021;Amador-Domínguez et al, 2021;Benarab et al, 2019;Chen et al, 2021;Wang et al, 2021bWang et al, , 2010Sabra et al, 2020;Pancerz and Lewicki, 2014;Yilmaz, 2017;Kumar et al, 2020;Rinaldi et al, 2021;Gomathi and Karlekar, 2019;Serafini et al, 2017;Kuang et al, 2021;Chung et al, 2020;Fu et al, 2015;Huang et al, 2019;Abdollahi et al, 2021;Ahani et al, 2021;Akila et al, 2021;Deepak et al, 2022;Messaoudi et al, 2021;Nayak et al, 2021;Zhao et al, 2021), especially Recurrent Neural Networks (Makni and Hendler, 2019;Ren et al, 2020;Moussallem et al, 2019;Zhang et al, 2019;Jang et al, 2018;Ali et al, 2021;Liu et al, 2021;Huang et al, 2019;Alexandridis et al, 2021;Niu...…”

Weibo Text Sentiment Analysis Based on BERT and Deep Learning

“…Gonzalez et al [19] proposed a sentiment analysis model based on methods such as CNN, RNN and polar dictionary, which verified the effectiveness of the model on English and Arabic datasets. Alexandridis et al [20] proposed a model that effectively recognizes negative emotions. This model combines traditional deep learning techniques and introduces an attention mechanism and a knowledge management system, which can further improve the accuracy of classification.…”