Aspect-Based Sentiment Analysis using Machine Learning and Deep Learning Approaches

Sentiment analysis (SA) is also known as opinion mining, it is the process of gathering and analyzing people's opinions about a particular service, good, or company on websites like Twitter, Facebook, Instagram, LinkedIn, and blogs, among other places. This article covers a thorough analysis of SA and its levels. This manuscript's main focus is on aspect-based SA, which helps manufacturing organizations make better decisions by examining consumers' viewpoints and opinions of their products. The many approaches and methods used in aspect-based sentiment analysis are covered in this review study (ABSA). The features associated with the aspects were manually drawn out in traditional methods, which made it a time-consuming and error-prone operation. Nevertheless, these restrictions may be overcome as artificial intelligence develops. Therefore, to increase the effectiveness of ABSA, researchers are increasingly using AI-based machine learning (ML) and deep learning (DL) techniques. Additionally, certain recently released ABSA approaches based on ML and DL are examined, contrasted, and based on this research, gaps in both methodologies are discovered. At the conclusion of this study, the difficulties that current ABSA models encounter are also emphasized, along with suggestions that can be made to improve the efficacy and precision of ABSA systems

On the Robustness of Aspect-based Sentiment Analysis: Rethinking Model, Data, and Training

Aspect-based sentiment analysis (ABSA) aims at automatically inferring the specific sentiment polarities toward certain aspects of products or services behind the social media texts or reviews, which has been a fundamental application to the real-world society. Since the early 2010s, ABSA has achieved extraordinarily high accuracy with various deep neural models. However, existing ABSA models with strong in-house performances may fail to generalize to some challenging cases where the contexts are variable, i.e., low robustness to real-world environments. In this study, we propose to enhance the ABSA robustness by systematically rethinking the bottlenecks from all possible angles, including model, data, and training. First, we strengthen the current best-robust syntax-aware models by further incorporating the rich external syntactic dependencies and the labels with aspect simultaneously with a universal-syntax graph convolutional network. In the corpus perspective, we propose to automatically induce high-quality synthetic training data with various types, allowing models to learn sufficient inductive bias for better robustness. Last, we based on the rich pseudo data perform adversarial training to enhance the resistance to the context perturbation and meanwhile employ contrastive learning to reinforce the representations of instances with contrastive sentiments. Extensive robustness evaluations are conducted. The results demonstrate that our enhanced syntax-aware model achieves better robustness performances than all the state-of-the-art baselines. By additionally incorporating our synthetic corpus, the robust testing results are pushed with around 10% accuracy, which are then further improved by installing the advanced training strategies. In-depth analyses are presented for revealing the factors influencing the ABSA robustness.Comment: Accepted in ACM Transactions on Information System

Interpretable Architectures and Algorithms for Natural Language Processing

Paper V is excluded from the dissertation with respect to copyright.This thesis has two parts: Firstly, we introduce the human level-interpretable models using Tsetlin Machine (TM) for NLP tasks. Secondly, we present an interpretable model using DNNs. The first part combines several architectures of various NLP tasks using TM along with its robustness. We use this model to propose logic-based text classification. We start with basic Word Sense Disambiguation (WSD), where we employ TM to design novel interpretation techniques using the frequency of words in the clause. We then tackle a new problem in NLP, i.e., aspect-based text classification using a novel feature engineering for TM. Since TM operates on Boolean features, it relies on Bag-of-Words (BOW), making it difficult to use pre-trained word embedding like Glove, word2vec, and fasttext. Hence, we designed a Glove embedded TM to significantly enhance the model’s performance. In addition to this, NLP models are sensitive to distribution bias because of spurious correlations. Hence we employ TM to design a robust text classification against spurious correlations. The second part of the thesis consists interpretable model using DNN where we design a simple solution for complex position dependent NLP task. Since TM’s interpretability comes with the cost of performance, we propose an DNN-based architecture using a masking scheme on LSTM/GRU based models that ease the interpretation for humans using the attention mechanism. At last, we take the advantages of both models and design an ensemble model by integrating TM’s interpretable information into DNN for better visualization of attention weights. Our proposed model can be efficiently integrated to have a fully explainable model for NLP that assists trustable AI. Overall, our model shows excellent results and interpretation in several open-sourced NLP datasets. Thus, we believe that by combining the novel interpretation of TM, the masking technique in the neural network, and the integrated ensemble model, we can build a simple yet effective platform for explainable NLP applications wherever necessary.publishedVersio

Multimodal sentiment analysis in real-life videos

This thesis extends the emerging field of multimodal sentiment analysis of real-life videos, taking two components into consideration: the emotion and the emotion's target. The emotion component of media is traditionally represented as a segment-based intensity model of emotion classes. This representation is replaced here by a value- and time-continuous view. Adjacent research fields, such as affective computing, have largely neglected the linguistic information available from automatic transcripts of audio-video material. As is demonstrated here, this text modality is well-suited for time- and value-continuous prediction. Moreover, source-specific problems, such as trustworthiness, have been largely unexplored so far. This work examines perceived trustworthiness of the source, and its quantification, in user-generated video data and presents a possible modelling path. Furthermore, the transfer between the continuous and discrete emotion representations is explored in order to summarise the emotional context at a segment level. The other component deals with the target of the emotion, for example, the topic the speaker is addressing. Emotion targets in a video dataset can, as is shown here, be coherently extracted based on automatic transcripts without limiting a priori parameters, such as the expected number of targets. Furthermore, alternatives to purely linguistic investigation in predicting targets, such as knowledge-bases and multimodal systems, are investigated. A new dataset is designed for this investigation, and, in conjunction with proposed novel deep neural networks, extensive experiments are conducted to explore the components described above. The developed systems show robust prediction results and demonstrate strengths of the respective modalities, feature sets, and modelling techniques. Finally, foundations are laid for cross-modal information prediction systems with applications to the correction of corrupted in-the-wild signals from real-life videos

Empirical Evaluation Methodology for Target Dependent Sentiment Analysis

The area of sentiment analysis has been around for at least 20 years in one form or another. In which time, it has had many and varied applications ranging from predicting film successes to social media analytics, and it has gained widespread use via selling it as a tool through application programming interfaces. The focus of this thesis is not on the application side but rather on novel evaluation methodology for the most fine grained form of sentiment analysis, target dependent sentiment analysis (TDSA). TDSA has seen a recent upsurge but to date most research only evaluates on very similar datasets which limits the conclusions that can be drawn from it. Further, most research only marginally improves results, chasing the State Of The Art (SOTA), but these prior works cannot empirically show where their improvements come from beyond overall metrics and small qualitative examples. By performing an extensive literature review on the different granularities of sentiment analysis, coarse (document level) to fine grained, a new and extended definition of fine grained sentiment analysis, the hextuple, is created which removes ambiguities that can arise from the context. In addition, examples from the literature will be provided where studies are not able to be replicated nor reproduced. This thesis includes the largest empirical analysis on six English datasets across multiple existing neural and non-neural methods, allowing for the methods to be tested for generalisability. In performing these experiments factors such as dataset size and sentiment class distribution determine whether neural or non-neural approaches are best, further finding that no method is generalisable. By formalising, analysing, and testing prior TDSA error splits, newly created error splits, and a new TDSA specific metric, a new empirical evaluation methodology has been created for TDSA. This evaluation methodology is then applied to multiple case studies to empirically justify improvements, such as position encoding, and show how contextualised word representation improves TDSA methods. From the first reproduction study in TDSA, it is believed that random seeds significantly affecting the neural method is the reason behind the difficulty in reproducing or replicating the original study results. Thus highlighting empirically for the first in TDSA the need for reporting multiple run results for neural methods, to allow for better reporting and improved evaluation. This thesis is fully reproducible through the codebases and Jupyter notebooks referenced, making it an executable thesis

Neural Approaches to Relational Aspect-Based Sentiment Analysis. Exploring generalizations across words and languages

Jebbara S. Neural Approaches to Relational Aspect-Based Sentiment Analysis. Exploring generalizations across words and languages. Bielefeld: Universität Bielefeld; 2020.Everyday, vast amounts of unstructured, textual data are shared online in digital form. Websites such as forums, social media sites, review sites, blogs, and comment sections offer platforms to express and discuss opinions and experiences. Understanding the opinions in these resources is valuable for e.g. businesses to support market research and customer service but also individuals, who can benefit from the experiences and expertise of others. In this thesis, we approach the topic of opinion extraction and classification with neural network models. We regard this area of sentiment analysis as a relation extraction problem in which the sentiment of some opinion holder towards a certain aspect of a product, theme, or event needs to be extracted. In accordance with this framework, our main contributions are the following: 1. We propose a full system addressing all subtasks of relational sentiment analysis. 2. We investigate how semantic web resources can be leveraged in a neural-network-based model for the extraction of opinion targets and the classification of sentiment labels. Specifically, we experiment with enhancing pretrained word embeddings using the lexical resource WordNet. Furthermore, we enrich a purely text-based model with SenticNet concepts and observe an improvement for sentiment classification. 3. We examine how opinion targets can be automatically identified in noisy texts. Customer reviews, for instance, are prone to contain misspelled words and are difficult to process due to their domain-specific language. We integrate information about the character structure of a word into a sequence labeling system using character-level word embeddings and show their positive impact on the system's performance. We reveal encoded character patterns of the learned embeddings and give a nuanced view of the obtained performance differences. 4. Opinion target extraction usually relies on supervised learning approaches. We address the lack of available annotated data for specific languages by proposing a zero-shot cross-lingual approach for the extraction of opinion target expressions. We leverage multilingual word embeddings that share a common vector space across various languages and incorporate these into a convolutional neural network architecture. Our experiments with 5 languages give promising results: We can successfully train a model on annotated data of a source language and perform accurate prediction on a target language without ever using any annotated samples in that target language

Aspect-invariant sentiment feature learning : adversarial multi-task learning for aspect-based sentiment analysis

In most previous studies, the aspect-related text is considered an important clue for the Aspect-based Sentiment Analysis (ABSA) task, and thus various attention mechanisms have been proposed to leverage the interactions between aspects and context. However, it is observed that some sentiment expressions carry the same polarity regardless of the aspects they are associated with. In such cases, it is not necessary to incorporate aspect information for ABSA. More observations on the experimental results show that blindly leveraging interactions between aspects and context as features may introduce noises when analyzing those aspect-invariant sentiment expressions, especially when the aspect-related annotated data is insufficient. Hence, in this paper, we propose an Adversarial Multi-task Learning framework to identify the aspect-invariant/dependent sentiment expressions without extra annotations. In addition, we adopt a gating mechanism to control the contribution of representations derived from aspect-invariant and aspect-dependent hidden states when generating the final contextual sentiment representations for the given aspect. This essentially allows the exploitation of aspect-invariant sentiment features for better ABSA results. Experimental results on two benchmark datasets show that extending existing neural models using our proposed framework achieves superior performance. In addition, the aspect-invariant data extracted by the proposed framework can be considered as pivot features for better transfer learning of the ABSA models on unseen aspects