Existing phrase embeddings provide low dimensional representations for phrases or sentences. However, these phrase embedding techniques treat phrases as independent units and are unable to capture contextual and sentiment dependencies among them. As a result, phrases with similar contexts and sentiments could have unrelated embedding representations. In order to overcome this research gap, we have proposed a method to infer context-aware causal phrase embeddings using a multi-relational graph neural network. Initially, a sequence-labelling deep model is used to extract rationale (causal phrases) for user opinions and a multi-relational causal graph is constructed, which is input to a graph neural network to infer context-aware causal phrase embeddings. The context enriched multi-relational embeddings are then fed to a neural network classifier to predict sentiment polarities. We have evaluated our models on three publicly available datasets and obtained mean accuracies of 96%, 95%, 80% and 95%, 95%, 80% respectively for causality extraction and sentiment inference tasks. Empirical evaluation reveals that the models proposed could outperform strong baselines in the literature with respect to causal extraction and sentiment inference tasks.

Traditional sentiment-aware topic models assume that topic or sentiment transition occurs from either a sentence to the next sentence or from a word to the next word. Such models cannot capture a topic or sentiment transition at phrase boundaries. Further, most of the models adopt a sentiment lexicon to initialize sentiment priors and this approach induces coverage problems. To overcome the above-cited limitations, we have proposed a topic model that extracts aspects, sentiments, and causal phrases simultaneously by leveraging Hierarchical Pitman Yor Process (HPYP) that is modified using a sentiment component, a word-tagger to guide the causal phrase generation and a sentiment prior initialized through a sequential model to address coverage problems. We have evaluated our model on six datasets and found that the proposed model outperforms the baselines in terms of perplexity by 14%, topical coherence by 20%, topic diversity by 5%, sentiment classification task's accuracy by 4% and, precision, recall and F1 score by 2%. Ablation studies assert that sequence model based sentiment prior initialization results in increasing the accuracy of sentiment classification by 2%.

Aspect-level sentiment analysis gives a detailed view of user opinions expressed towards each feature of a product. Aspect extraction is a challenging task in aspect-level sentiment analysis. Hence, several researchers worked on the problem of aspect extraction during the past decade. The authors begin this chapter with a brief introduction to aspect-level sentimental analysis, which covers the definition of key terms used in this chapter, and the authors also illustrate various subtasks of aspect-level sentiment analysis. The introductory section is followed by an explanation of the various feature learning methods like supervised, unsupervised, semi-supervised, etc. with a discussion regarding their merits and demerits. The authors compare the aspect extraction methods performance with respect to metrics and a detailed discussion on the merits and demerits of the approaches. They conclude the chapter with pointers to the unexplored problems in aspect-level sentiment analysis that may be beneficial to the researchers who wish to pursue work in this challenging and mature domain.

Sentiment analysis is used to extract opinions expressed in product reviews. Aspect level sentiment analysis extracts opinions about features of a product. However, such analysis cannot infer the underlying reason for the opinions expressed. The following useful applications can be realized, if reasons for opinions are inferred: (i) To perform root cause analysis of negative opinions. (ii) Sentiment inference of causes helps in building a phrase-level sentiment lexicon, and (iii) Creation of causality-aware word embeddings to enhance the accuracy of sentiment analyzers. To realize above-cited use-cases, we have proposed to use a deep neural sequence model to extract cause phrases and designed a novel hybrid model based on graph neural networks and Bayesian reasoning for inferring the sentiments implied by cause phrases. We tested our models on three annotated datasets and observed mean accuracies of 96.34%, 96.12%, 97.14% and 82.14%, 85.23%, 87.21% for the cause phrase extraction and sentiment inference tasks respectively. We have also investigated the impact of over smoothing in graph neural network through an ablation study and reported the results.

Aspect-level sentiment analysis gives a detailed view of user opinions expressed towards each feature of a product. Aspect extraction is a challenging task in aspect-level sentiment analysis. Hence, several researchers worked on the problem of aspect extraction during the past decade. The authors begin this chapter with a brief introduction to aspect-level sentimental analysis, which covers the definition of key terms used in this chapter, and the authors also illustrate various subtasks of aspect-level sentiment analysis. The introductory section is followed by an explanation of the various feature learning methods like supervised, unsupervised, semi-supervised, etc. with a discussion regarding their merits and demerits. The authors compare the aspect extraction methods performance with respect to metrics and a detailed discussion on the merits and demerits of the approaches. They conclude the chapter with pointers to the unexplored problems in aspect-level sentiment analysis that may be beneficial to the researchers who wish to pursue work in this challenging and mature domain. © 2021, IGI Global.