Effectiveness of Domain-Based Lexicons vis-à-vis General Lexicon for Aspect-Level Sentiment Analysis: A Comparative Analysis

Abstract

One can either use machine learning techniques or lexicons to undertake sentiment analysis. Machine learning techniques include text classification algorithms like SVM, naive Bayes, decision tree or logistic regression, whereas lexicon-based sentiment analysis uses either general or domain-based lexicons. In this paper, we investigate the effectiveness of domain lexicons vis-à-vis general lexicon, wherein we have performed aspect-level sentiment analysis on data from three different domains, viz. car, guitar and book. While it is intuitive that domain lexicons will always perform better than general lexicons, the actual performance however may depend on the richness of the concerned domain lexicon as well as the text analysed. We used the general lexicon SentiWordNet and the corresponding domain lexicons in the aforesaid domains to compare their relative performances. The results indicate that domain lexicon used along with general lexicon performs better as compared to general lexicon or domain lexicon, when used alone. They also suggest that the performance of domain lexicons depends on the text content; and also on whether the language involves technical or non-technical words in the concerned domain. This paper makes a case for development of domain lexicons across various domains for improved performance, while gathering that they might not always perform better. It further highlights that the importance of general lexicons cannot be underestimated — the best results for aspect-level sentiment analysis are obtained, as per this paper, when both the domain and general lexicons are used side by side.

Aspect-Based Sentiment Classification Using Deep Learning and Hybrid of Word Embedding and Contextual Position

“…These are laborious, complicated, and time-consuming approaches that require a lot of effort [22,[27][28][29][30]. In the early days of ABSA, machine learning methods, such as semi-supervised [31,32], supervised [33,34], and unsupervised [35], lexicons-based approaches (i.e., domain-based lexicons and SentiWordNets) [36][37][38][39][40], rule-based or pattern-based techniques [41][42][43][44][45][46][47][48], topic modelling based procedures [49][50][51], and tree or graph-based strategies [52,53] accomplished the feature and sentiment extraction task. Today, DL methods are famous for ABSA tasks, but including reasoning like the human brain remains an open research area.…”

Attention-Based Multi-Channel Gated Recurrent Neural Networks: A Novel Feature-Centric Approach for Aspect-Based Sentiment Classification

“…In this situation, ABSA fulfils this gap and handles these scenarios with enough ability which expresses the liked and disliked features of a targeted entity. It associates the sentiment polarity with specific aspects or features of any targeted entity [2], [21].…”

“…These are complicated, laborious, and time-consuming techniques which demand a lot of effort from analysts [12], [14], [23], [24], [25]. Moreover, the feature extraction task accomplishes through machine learning approaches such as supervised [26], [27], semi-supervised [28], [29], and unsupervised [30], lexicons-based methods, e.g., SentiWordNet and domain-based lexicons [2], [31], [32], [33], [34], rulebased or pattern-based approaches [7], [35], [36], [37], [38], [39], [40], [41], topic modelling based techniques [42], [43], comprising MC-CNN that merges three vector representations to perform the task of ABSA. These incorporate GloVe, word2vec, and a one-hot character-based embedding.…”

“…An unbiased automatic examination of these bulky textual contents that assist people in responding to people's reflections requires a system that collectively uses Natural Language Processing (NLP), linguistically computation, and text retrieval to classify feelings and their expressed sentiments. Thus, the analysis that utilizes NLP and its linguistic computations for subjectivity extraction from a textual context is known as Sentiment Analysis (SA) [2], [3]. It can also describe as a web-based unstructured textual content computational analysis, which can analyze the attitude, behavior, and thoughts criteria belonging to a person about a specific entity.…”

Review of sentiment analysis: An emotional product development view

“…In general, distance, syntactic dependency, and co-occurrence among them are always used to extract the opinion words of an aspect. When opinion words are extracted from the context at a certain distance from a specific aspect, we call it a distance-based technique (Nasim and Haider, 2017;Yadav and Roychoudhury, 2019). If opinion words modifying a specific aspect are extracted using the dependency parsing algorithm, we call it a dependency parsing technique (Quan and Ren, 2014;Afzaal et al, 2019b;Jiao and Qu, 2019;Wang et al, 2019a;2019c).…”