Conventional and Structure Based Sentiment Analysis: A Survey

Abstract

Sentiment Analysis is a strand of Natural Language Processing that deals with the emotional polarity a given piece of text has. To gain this understanding from just a string of words, we must first consider a suitable way to break down the text to further classify what each part means. This can be done in a plethora of ways, which mostly stem from the understanding of a classifier. We believe that there is a large amount of information stored in structure-based features within the text, for instance, where the writer may place negation-terms not, neither and how this affects the overall polarity. Similarly, how words in sentences or sections, remote to the current-analysed section, may affect the polarity of said section. A combination of features from both a conventional and a structure-based understanding may also provide us with a larger accuracy in polarity. Therefore, this paper aims to explain both conventional sentiment analysis methods with structure-based methods as well as their practices, advantages and disadvantages concluding with how sentiment analysis can move forward with the appropriation of hybrid methods (methods involving motifs, practices and understandings) from conventional and structure-based methods, for classification.

Attention-based Sentence Extraction for Aspect-based Sentiment Analysis with Implicit Aspect Cases in Hotel Review Using Machine Learning Algorithm, Semantic Similarity, and BERT

“…Text extraction for a review of implicit aspects, requires in-depth attention regarding the structure of the words in the sentence. The sentences that contain implicit aspects that can have an impact on the level of word sense disambiguation can be influenced by several factors [7] consist: negation (conjunction analysis and punctuation marks); contrast transition; and intensifiers. One of the factors that can affects the level of disambiguity of a sentence so impact on error in extracting the aspect term.…”

“…However, the type of implicit commentary that is influenced by the presence of ambiguous sentence factors has not been worked out in this study. These factors consist of negation, contrast transition, or intensifiers [7].…”